From 8ebd8983ac70109deae2a6cd21b768deb940d3ef Mon Sep 17 00:00:00 2001 From: Joris Van den Bossche Date: Sun, 4 Dec 2022 15:52:01 +0100 Subject: [PATCH 1/4] update slides --- docs/slides.html | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/slides.html b/docs/slides.html index d8ec8e8..1f9d3d1 100644 --- a/docs/slides.html +++ b/docs/slides.html @@ -13,7 +13,7 @@ # Data manipulation, analysis and visualisation in Python Specialist course Doctoral schools of Ghent University
-June 8, 9 and 10, 2022 +December 5, 12 and 19 2022 Joris Van den Bossche, Stijn Van Hoey @@ -24,9 +24,9 @@ # Who are you? -Go to https://hackmd.io/ye6LZ9hAR-22PGiNT2OCMQ?both +Go to https://hackmd.io/vBEsylODTfmhjxVTY_0XPw?both - + --- @@ -69,14 +69,14 @@ class: left, middle 0. Everyone has conda installed and the environment setup? If not, see [1-install-python-and-the-required-python-packages](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html#1-install-python-and-the-required-python-packages) -1. Make sure to (re)download ALL the course material, see [2-getting-the-course-materials](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html#2-getting-the-course-materials) also if you already did this before. +1. Make sure to (re)download ALL the course material, see [2-getting-the-course-materials](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html#2-first-day-of-the-course-getting-the-course-materials) also if you already did this before. 2. Next, also do section 3 and 4 of the [setup](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html) > If you succesfully done 1, 2 and 3, put up your `green sticky note` on your laptop screen.. Next: -- Surf to and fill in [the questionnaire](https://hackmd.io/ye6LZ9hAR-22PGiNT2OCMQ?both) +- Surf to and fill in [the questionnaire](https://hackmd.io/vBEsylODTfmhjxVTY_0XPw?both) - In Jupyter Lab, start with the 'notebooks/00-jupyter_introduction.ipynb'. > Installation or setup issues? Put up your `red sticky note` on your laptop screen. @@ -211,7 +211,7 @@ List all your environments: `conda info -e` -See the docs: http://conda.pydata.org/docs/using/index.html +See the docs: https://docs.conda.io/projects/conda/en/latest/user-guide/index.html --- class: center, middle @@ -302,7 +302,7 @@ ![:scale 100%](http://esq.h-cdn.co/assets/15/51/980x490/landscape-1450137389-john-cleese.JPG) -### https://forms.gle/R47g9xaJ4hRGtajN9 +### https://forms.gle/dWEkBF4znai4Sh5e8 Please fill in the questionnaire! From 5593d03af2db310a28522718ce1ad5f139179110 Mon Sep 17 00:00:00 2001 From: Joris Van den Bossche Date: Sun, 4 Dec 2022 19:45:52 +0100 Subject: [PATCH 2/4] generated notebooks --- _solved/00-jupyter_introduction.ipynb | 18 +- _solved/case1_bike_count.ipynb | 10 +- _solved/case2_observations.ipynb | 8 +- _solved/case2_observations_analysis.ipynb | 4 +- _solved/case2_observations_processing.ipynb | 4 +- ..._bacterial_resistance_lab_experiment.ipynb | 14 +- _solved/case4_air_quality_analysis.ipynb | 4 +- _solved/case4_air_quality_processing.ipynb | 4 +- _solved/pandas_01_data_structures.ipynb | 6 +- _solved/pandas_02_basic_operations.ipynb | 6 +- _solved/pandas_03a_selecting_data.ipynb | 20 +- _solved/pandas_03b_indexing.ipynb | 6 +- _solved/pandas_04_time_series_data.ipynb | 45 +- _solved/pandas_05_groupby_operations.ipynb | 30 +- _solved/pandas_06_data_cleaning.ipynb | 7 +- _solved/pandas_07_missing_values.ipynb | 8 +- _solved/pandas_08_reshaping_data.ipynb | 25 +- _solved/pandas_09_combining_datasets.ipynb | 16 +- _solved/visualization_01_matplotlib.ipynb | 25 +- _solved/visualization_02_plotnine.ipynb | 4 +- _solved/visualization_02_seaborn.ipynb | 108 +- _solved/visualization_03_landscape.ipynb | 19 +- notebooks/00-jupyter_introduction.ipynb | 18 +- ...3_bacterial_resistance_lab_experiment10.py | 5 +- .../_solutions/pandas_01_data_structures5.py | 2 +- .../_solutions/pandas_02_basic_operations2.py | 2 +- .../_solutions/pandas_03a_selecting_data13.py | 2 +- .../_solutions/pandas_03a_selecting_data14.py | 2 +- .../_solutions/pandas_03a_selecting_data15.py | 2 +- .../_solutions/pandas_03a_selecting_data16.py | 2 +- .../_solutions/pandas_03a_selecting_data17.py | 2 +- .../_solutions/pandas_03a_selecting_data18.py | 2 +- .../_solutions/pandas_03a_selecting_data19.py | 2 +- .../_solutions/pandas_03a_selecting_data20.py | 2 +- .../_solutions/pandas_03a_selecting_data21.py | 4 +- .../_solutions/pandas_03a_selecting_data22.py | 7 +- .../_solutions/pandas_03a_selecting_data23.py | 4 + .../pandas_05_groupby_operations10.py | 2 +- .../pandas_05_groupby_operations5.py | 2 +- .../pandas_05_groupby_operations6.py | 2 +- .../pandas_05_groupby_operations8.py | 2 +- .../pandas_05_groupby_operations9.py | 2 +- .../_solutions/pandas_08_reshaping_data15.py | 3 +- .../_solutions/pandas_08_reshaping_data17.py | 2 +- .../_solutions/pandas_08_reshaping_data5.py | 2 +- .../_solutions/pandas_08_reshaping_data8.py | 8 +- .../_solutions/visualization_02_seaborn10.py | 14 +- .../_solutions/visualization_02_seaborn11.py | 12 +- .../_solutions/visualization_02_seaborn12.py | 17 +- .../_solutions/visualization_02_seaborn13.py | 8 +- .../_solutions/visualization_02_seaborn14.py | 17 +- .../_solutions/visualization_02_seaborn15.py | 6 +- .../_solutions/visualization_02_seaborn16.py | 22 +- .../_solutions/visualization_02_seaborn17.py | 8 +- .../_solutions/visualization_02_seaborn18.py | 21 +- .../_solutions/visualization_02_seaborn19.py | 5 +- .../_solutions/visualization_02_seaborn20.py | 5 +- .../_solutions/visualization_02_seaborn21.py | 1 + .../_solutions/visualization_02_seaborn22.py | 3 + .../_solutions/visualization_02_seaborn8.py | 15 +- .../_solutions/visualization_02_seaborn9.py | 13 +- notebooks/case1_bike_count.ipynb | 10 +- notebooks/case2_observations.ipynb | 8 +- notebooks/case2_observations_analysis.ipynb | 1274 ---------- notebooks/case2_observations_processing.ipynb | 2229 ----------------- ..._bacterial_resistance_lab_experiment.ipynb | 9 +- notebooks/case4_air_quality_analysis.ipynb | 4 +- notebooks/case4_air_quality_processing.ipynb | 4 +- notebooks/pandas_01_data_structures.ipynb | 4 +- notebooks/pandas_02_basic_operations.ipynb | 4 +- notebooks/pandas_03a_selecting_data.ipynb | 40 +- notebooks/pandas_03b_indexing.ipynb | 6 +- notebooks/pandas_04_time_series_data.ipynb | 45 +- notebooks/pandas_05_groupby_operations.ipynb | 20 +- notebooks/pandas_06_data_cleaning.ipynb | 7 +- notebooks/pandas_07_missing_values.ipynb | 8 +- notebooks/pandas_08_reshaping_data.ipynb | 10 +- notebooks/pandas_09_combining_datasets.ipynb | 16 +- notebooks/visualization_01_matplotlib.ipynb | 25 +- notebooks/visualization_02_seaborn.ipynb | 149 +- notebooks/visualization_03_landscape.ipynb | 19 +- 81 files changed, 539 insertions(+), 3993 deletions(-) create mode 100644 notebooks/_solutions/pandas_03a_selecting_data23.py create mode 100644 notebooks/_solutions/visualization_02_seaborn21.py create mode 100644 notebooks/_solutions/visualization_02_seaborn22.py delete mode 100644 notebooks/case2_observations_analysis.ipynb delete mode 100644 notebooks/case2_observations_processing.ipynb diff --git a/_solved/00-jupyter_introduction.ipynb b/_solved/00-jupyter_introduction.ipynb index ec80c8b..7fddc4c 100644 --- a/_solved/00-jupyter_introduction.ipynb +++ b/_solved/00-jupyter_introduction.ipynb @@ -6,7 +6,7 @@ "source": [ "

Jupyter notebook INTRODUCTION

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -186,20 +186,6 @@ "#### subsubsection" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Raw Text" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Cfr. any text editor" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -554,7 +540,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/case1_bike_count.ipynb b/_solved/case1_bike_count.ipynb index 9fbeece..d72e931 100644 --- a/_solved/case1_bike_count.ipynb +++ b/_solved/case1_bike_count.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - Bike count data

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -187,8 +187,8 @@ "\n", "Pre-process the data:\n", "\n", - "* Combine the 'datum' and 'tijd' columns into one Pandas Series of string datetime values, call this new variable `combined`.\n", - "* Parse the string datetime values to `datetime` objects.\n", + "* Combine the 'datum' and 'tijd' columns into one Pandas Series of string values, call this new variable `combined`.\n", + "* Parse the string values to `datetime` objects.\n", "* Set the resulting `datetime` column as the index of the `df` DataFrame.\n", "* Remove the original 'datum' and 'tijd' columns using the `drop` method, and call the new dataframe `df2`.\n", "* Rename the columns in the DataFrame 'ri Centrum', 'ri Mariakerke' to resp. 'direction_centre', 'direction_mariakerke' using the `rename` method.\n", @@ -1148,7 +1148,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So the daily pattern is clearly different for both directions. In the morning more people go north, in the evening more people go south. The morning peak is also more condensed." + "So the daily pattern is clearly different for both directions. In the morning more people go towards the centre, in the evening more people go back to Mariakerke. The morning peak is also more condensed." ] }, { @@ -1220,7 +1220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/case2_observations.ipynb b/_solved/case2_observations.ipynb index 1354f92..d6fbb51 100644 --- a/_solved/case2_observations.ipynb +++ b/_solved/case2_observations.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - Observation data

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -309,11 +309,11 @@ "\n", "**EXERCISE**\n", "\n", - "How many records in the data set have no information about the `species`? Use the `isna()` method to find out.\n", + "How many records in the data set have no information about the `species_ID`? Use the `isna()` method to find out.\n", "\n", "
Hints\n", "\n", - "- Do NOT use `survey_data_processed['species'] == np.nan`, but use the available method `isna()` to check if a value is NaN\n", + "- Do NOT use `survey_data_processed['species_ID'] == np.nan`, but use the available method `isna()` to check if a value is NaN\n", "- The result of an (element-wise) condition returns a set of True/False values, corresponding to 1/0 values. The amount of True values is equal to the sum.\n", " \n", "
" @@ -1673,7 +1673,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.8.13" }, "nav_menu": {}, "toc": { diff --git a/_solved/case2_observations_analysis.ipynb b/_solved/case2_observations_analysis.ipynb index c6d0c5f..596ac16 100644 --- a/_solved/case2_observations_analysis.ipynb +++ b/_solved/case2_observations_analysis.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - Observation data - analysis

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -1319,7 +1319,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/case2_observations_processing.ipynb b/_solved/case2_observations_processing.ipynb index f886680..3304898 100644 --- a/_solved/case2_observations_processing.ipynb +++ b/_solved/case2_observations_processing.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - Observation data - data cleaning and enrichment

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -2274,7 +2274,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/case3_bacterial_resistance_lab_experiment.ipynb b/_solved/case3_bacterial_resistance_lab_experiment.ipynb index 7a94c1d..ef62186 100644 --- a/_solved/case3_bacterial_resistance_lab_experiment.ipynb +++ b/_solved/case3_bacterial_resistance_lab_experiment.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - Bacterial resistance experiment

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -525,14 +525,13 @@ "- Make sure the `WT(2)` and `MUT(2)` categories are used as respectively `WT` and `MUT` by adjusting them with Pandas first.\n", "- Use the __falcor__ data and the Seaborn package. The 'log10 mutation rate' on the figure corresponds to the `log10 Mc` column.\n", "\n", - "\n", "
Hints\n", "\n", "- To replace values using a mapping (dictionary with the keys the current values and the values the new values), use the Pandas `replace` method.\n", "- This is another example of a `catplot`, using `point`s to represent the data.\n", "- The `join` argument defines if individual points need to be connected or not.\n", - "- One combination appears multiple times, so make sure to not yet use confidence intervals by setting `ci` to `Null`.\n", - "\n", + "- Seaborn provides the `row_order` and `order` options to provide a custom order of respectively the subplots and categories.\n", + "- One combination appears multiple times, so make sure to not yet use confidence intervals by setting `errorbar` to `None`.\n", "
" ] }, @@ -568,7 +567,10 @@ " x='Bacterial_genotype',\n", " y='log10 Mc',\n", " row=\"Phage\",\n", - " join=False, ci=None,\n", + " join=False, \n", + " errorbar=None,\n", + " row_order=[\"Lambda\", \"T4\", \"T7\"],\n", + " order=['WT', 'MUT', 'D87G', 'S83L', 'D516G', 'S512F', 'K43N', 'K88R', 'RSF1010', 'RP4'],\n", " aspect=3, height=3,\n", " color=\"black\")" ] @@ -679,7 +681,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/case4_air_quality_analysis.ipynb b/_solved/case4_air_quality_analysis.ipynb index 3a8a75a..1a2b361 100644 --- a/_solved/case4_air_quality_analysis.ipynb +++ b/_solved/case4_air_quality_analysis.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - air quality data of European monitoring stations (AirBase)

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -1329,7 +1329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/case4_air_quality_processing.ipynb b/_solved/case4_air_quality_processing.ipynb index 721ad9d..ee2a03d 100644 --- a/_solved/case4_air_quality_processing.ipynb +++ b/_solved/case4_air_quality_processing.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - air quality data of European monitoring stations (AirBase)

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -771,7 +771,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/pandas_01_data_structures.ipynb b/_solved/pandas_01_data_structures.ipynb index b32793c..544be13 100644 --- a/_solved/pandas_01_data_structures.ipynb +++ b/_solved/pandas_01_data_structures.ipynb @@ -7,7 +7,7 @@ "source": [ "

01 - Pandas: Data Structures

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -702,7 +702,7 @@ }, "outputs": [], "source": [ - "df['Fare'].plot(kind='box') # or .plot.box()" + "df['Fare'].plot.box() # or .plot(kind='box')" ] }, { @@ -765,7 +765,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_02_basic_operations.ipynb b/_solved/pandas_02_basic_operations.ipynb index 153183a..6840a62 100644 --- a/_solved/pandas_02_basic_operations.ipynb +++ b/_solved/pandas_02_basic_operations.ipynb @@ -8,7 +8,7 @@ "

02 - Pandas: Basic operations on Series and DataFrames

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -349,7 +349,7 @@ }, "outputs": [], "source": [ - "df['Age'].hist() #bins=30, log=True" + "df['Age'].plot.hist() # bins=30, log=True)" ] }, { @@ -780,7 +780,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_03a_selecting_data.ipynb b/_solved/pandas_03a_selecting_data.ipynb index bdb9a59..02172c2 100755 --- a/_solved/pandas_03a_selecting_data.ipynb +++ b/_solved/pandas_03a_selecting_data.ipynb @@ -8,7 +8,7 @@ "

03 - Pandas: Indexing and selecting data - part I

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -656,7 +656,7 @@ "id": "49d05bde", "metadata": {}, "source": [ - "For the quick ones among you, here are some more exercises with some larger dataframe with film data. These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so all credit to him!) and the datasets he prepared for that. You can download these data from here: [`titles.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [`cast.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." + "For the quick ones among you, here are some more exercises with some larger dataframe with film data. These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so all credit to him!) and the datasets he prepared for that. You can download these data from here: [titles.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [cast.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." ] }, { @@ -740,6 +740,20 @@ "titles.sort_values('year').head(2)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "95a214f4-2ce3-4f56-9dfa-e546acdf12a5", + "metadata": { + "tags": [ + "nbtutor-solution" + ] + }, + "outputs": [], + "source": [ + "titles.nsmallest(2, columns=\"year\")" + ] + }, { "cell_type": "markdown", "id": "1150e93f", @@ -1023,7 +1037,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_03b_indexing.ipynb b/_solved/pandas_03b_indexing.ipynb index 54aff3a..0505304 100644 --- a/_solved/pandas_03b_indexing.ipynb +++ b/_solved/pandas_03b_indexing.ipynb @@ -7,7 +7,7 @@ "source": [ "

03 - Pandas: Indexing and selecting data - part II

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -696,7 +696,7 @@ "formats": "ipynb,md:myst" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -710,7 +710,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_04_time_series_data.ipynb b/_solved/pandas_04_time_series_data.ipynb index b065917..e11752e 100644 --- a/_solved/pandas_04_time_series_data.ipynb +++ b/_solved/pandas_04_time_series_data.ipynb @@ -7,7 +7,7 @@ "source": [ "

04 - Pandas: Working with time series data

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -682,11 +682,10 @@ "source": [ "
\n", "\n", - "EXERCISE:\n", + "**EXERCISE:\n", + "\n", + "* select all data starting from 2012\n", "\n", - "
    \n", - "
  • select all data starting from 2012
    • \n", - "
\n", "
" ] }, @@ -711,11 +710,16 @@ "source": [ "
\n", "\n", - "EXERCISE:\n", + "**EXERCISE**:\n", + "\n", + "* select all data in January for all different years\n", + "\n", + "
Hints\n", + "\n", + "* Remember you can get information about the month of each timestamp using the `month` attribute of the DatetimeIndex.\n", + "\n", + "
\n", "\n", - "
    \n", - "
  • select all data in January for all different years
    • \n", - "
\n", "
" ] }, @@ -740,11 +744,16 @@ "source": [ "
\n", "\n", - "EXERCISE:\n", + "**EXERCISE**:\n", + "\n", + "* select all data in April, May and June for all different years\n", + "\n", + "
Hints\n", + "\n", + "* If you want to check for equality with multiple possible values (equal to x OR equal to y OR ...), the `isin()` method can be easier to use.\n", + "\n", + "
\n", "\n", - "
    \n", - "
  • select all data in April, May and June for all different years
    • \n", - "
\n", "
" ] }, @@ -995,14 +1004,6 @@ "source": [ "data['2013':'2013'].mean().plot(kind='barh')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a643acc-c5e9-458a-ad7f-a782bd45db5c", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1024,7 +1025,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_05_groupby_operations.ipynb b/_solved/pandas_05_groupby_operations.ipynb index 2540f6d..19dd665 100644 --- a/_solved/pandas_05_groupby_operations.ipynb +++ b/_solved/pandas_05_groupby_operations.ipynb @@ -5,10 +5,10 @@ "id": "804eea96", "metadata": {}, "source": [ - "

06 - Pandas: \"Group by\" operations

\n", + "

05 - Pandas: \"Group by\" operations

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -362,7 +362,7 @@ }, "outputs": [], "source": [ - "df.groupby('Pclass')['Survived'].mean().plot(kind='bar') #and what if you would compare the total number of survivors?" + "df.groupby('Pclass')['Survived'].mean().plot.bar() #and what if you would compare the total number of survivors?" ] }, { @@ -374,7 +374,7 @@ "\n", "**EXERCISE**:\n", "\n", - "* Make a bar plot to visualize the average Fare payed by people depending on their age. The age column is divided is separate classes using the `pd.cut()` function as provided below.\n", + "* Make a bar plot to visualize the average Fare payed by people depending on their age. The age column is divided in separate classes using the `pd.cut()` function as provided below.\n", "\n", "" ] @@ -400,7 +400,7 @@ }, "outputs": [], "source": [ - "df.groupby('AgeClass')['Fare'].mean().plot(kind='bar', rot=0)" + "df.groupby('AgeClass')['Fare'].mean().plot.bar(rot=0)" ] }, { @@ -503,7 +503,7 @@ "id": "580c1483", "metadata": {}, "source": [ - "Another way to obtain such counts, is to use the Series `value_counts` method:" + "Another way to obtain such counts, is to use the Series `value_counts` method (which additionally sorts from most to least occurring group):" ] }, { @@ -529,7 +529,7 @@ "id": "7e3afc02", "metadata": {}, "source": [ - "These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so credit to him!) and the datasets he prepared for that. You can download these data from here: [`titles.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [`cast.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." + "These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so credit to him!) and the datasets he prepared for that. You can download these data from here: [titles.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [cast.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." ] }, { @@ -586,11 +586,11 @@ "source": [ "
\n", "\n", - "EXERCISE:\n", + "**EXERCISE**:\n", + "\n", + "\n", + "* Using `groupby()`, plot the number of films that have been released each decade in the history of cinema.\n", "\n", - "
    \n", - "
  • Using `groupby()`, plot the number of films that have been released each decade in the history of cinema.
    • \n", - "
\n", "
" ] }, @@ -619,7 +619,7 @@ }, "outputs": [], "source": [ - "titles.groupby('decade').size().plot(kind='bar', color='green')" + "titles.groupby('decade').size().plot.bar(color='green')" ] }, { @@ -650,7 +650,7 @@ "source": [ "titles['decade'] = titles['year'] // 10 * 10\n", "hamlet = titles[titles['title'] == 'Hamlet']\n", - "hamlet.groupby('decade').size().plot(kind='bar', color=\"orange\")" + "hamlet.groupby('decade').size().plot.bar(color=\"orange\")" ] }, { @@ -681,7 +681,7 @@ "source": [ "titles['decade'] = titles['year'] // 10 * 10\n", "hamlet = titles[titles['title'].str.contains('Hamlet')]\n", - "hamlet.groupby('decade').size().plot(kind='bar', color=\"lightblue\")" + "hamlet.groupby('decade').size().plot.bar(color=\"lightblue\")" ] }, { @@ -1255,7 +1255,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_06_data_cleaning.ipynb b/_solved/pandas_06_data_cleaning.ipynb index 485ca4f..216c472 100644 --- a/_solved/pandas_06_data_cleaning.ipynb +++ b/_solved/pandas_06_data_cleaning.ipynb @@ -5,10 +5,9 @@ "id": "a4812285-6a36-429e-835d-5c5584c9e9e2", "metadata": {}, "source": [ - "

Pandas: Methods for data cleaning

\n", + "

06 - Pandas: Methods for data cleaning

\n", "\n", - "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -931,7 +930,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_07_missing_values.ipynb b/_solved/pandas_07_missing_values.ipynb index cd056a5..0869c2f 100644 --- a/_solved/pandas_07_missing_values.ipynb +++ b/_solved/pandas_07_missing_values.ipynb @@ -5,10 +5,10 @@ "id": "8bd0774d", "metadata": {}, "source": [ - "

08 - Pandas: Working with missing data

\n", + "

07 - Pandas: Working with missing data

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -312,7 +312,7 @@ "\n", "**REMEMBER**:
\n", "\n", - "* missing value indicator: `np.nan` (`NaN`)\n", + "* Missing value indicator: `np.nan` (`NaN`)\n", "* Reductions: skipped by default\n", "* Mathematical operations (eg `+`): propagate by default\n", "* Specific functions:\n", @@ -351,7 +351,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_08_reshaping_data.ipynb b/_solved/pandas_08_reshaping_data.ipynb index 01b9dcf..59c5155 100644 --- a/_solved/pandas_08_reshaping_data.ipynb +++ b/_solved/pandas_08_reshaping_data.ipynb @@ -5,10 +5,10 @@ "id": "a7d814f2", "metadata": {}, "source": [ - "

07 - Pandas: Tidy data and reshaping

\n", + "

08 - Pandas: Tidy data and reshaping

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -121,7 +121,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8aaf53e9-cfba-4d32-989b-9f928f5284a2", + "id": "6d54b4ec-7d96-4906-88df-16b06ac43d21", "metadata": {}, "outputs": [], "source": [ @@ -321,7 +321,7 @@ }, "outputs": [], "source": [ - "df_overall = df_tidy.groupby([\"time\", \"Energie\"]).sum() # or with .reset_index()\n", + "df_overall = df_tidy.groupby([\"time\", \"Energie\"])[[\"consumption\"]].sum() # or with .reset_index()\n", "df_overall.head()" ] }, @@ -749,10 +749,10 @@ "outputs": [], "source": [ "fig, ax1 = plt.subplots()\n", - "df.pivot_table(index='Pclass', columns='Sex', \n", - " values='Survived', aggfunc='mean').plot(kind='bar', \n", - " rot=0, \n", - " ax=ax1)\n", + "(df.pivot_table(index='Pclass', columns='Sex', \n", + " values='Survived', aggfunc='mean')\n", + " .plot.bar(rot=0, ax=ax1)\n", + ")\n", "ax1.set_ylabel('Survival ratio')" ] }, @@ -1042,7 +1042,7 @@ "id": "45ce16c2", "metadata": {}, "source": [ - "These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so credit to him!) and the datasets he prepared for that. You can download these data from here: [`titles.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [`cast.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." + "These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so credit to him!) and the datasets he prepared for that. You can download these data from here: [titles.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [cast.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." ] }, { @@ -1110,7 +1110,8 @@ "outputs": [], "source": [ "cast.pivot_table(index='year', columns='type', values=\"character\", aggfunc='count').plot() \n", - "# for values in using the , take a column with no Nan values in order to count effectively all values -> at this stage: aha-erlebnis about crosstab function(!)" + "# for the values column to use in the aggfunc, take a column with no NaN values in order to count effectively all values\n", + "# -> at this stage: aha-erlebnis about crosstab function(!)" ] }, { @@ -1153,7 +1154,7 @@ }, "outputs": [], "source": [ - "pd.crosstab(index=cast['year'], columns=cast['type']).plot(kind='area')" + "pd.crosstab(index=cast['year'], columns=cast['type']).plot.area()" ] }, { @@ -1257,7 +1258,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/pandas_09_combining_datasets.ipynb b/_solved/pandas_09_combining_datasets.ipynb index 6628a50..754caf0 100644 --- a/_solved/pandas_09_combining_datasets.ipynb +++ b/_solved/pandas_09_combining_datasets.ipynb @@ -5,10 +5,10 @@ "id": "be5c9d31", "metadata": {}, "source": [ - "

Pandas: Combining datasets Part I - concat

\n", + "

09 - Pandas: Combining datasets

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -738,7 +738,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_by_muni = df.groupby(\"CD_REFNIS\").sum()" + "df_by_muni = df.groupby(\"CD_REFNIS\")[['MS_NUM_VAT', 'MS_NUM_VAT_START', 'MS_NUM_VAT_STOP']].sum()" ] }, { @@ -924,14 +924,6 @@ "source": [ "pd.concat([countries2, country_economics], axis=\"columns\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c420d1a6-5cf6-4efa-868d-0c591459e4e1", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -953,7 +945,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/_solved/visualization_01_matplotlib.ipynb b/_solved/visualization_01_matplotlib.ipynb index 66a7202..15dbef5 100644 --- a/_solved/visualization_01_matplotlib.ipynb +++ b/_solved/visualization_01_matplotlib.ipynb @@ -6,7 +6,7 @@ "source": [ "

Visualization - Matplotlib

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -901,7 +901,7 @@ "- In the left subplot, make a bar chart of the minimal measured value for each of the station.\n", "- In the right subplot, make a bar chart of the maximal measured value for each of the station. \n", "\n", - "Add a title to the Figure containing 'Minimal and maximal discharge from 2009-01-01 till 2013-01-02'. Extract these dates from the data itself instead of hardcoding it.\n", + "Add a title to the Figure containing 'Minimal and maximal discharge from 2009-01-01 till 2013-01-02'. Extract these dates from the data itself instead of hardcoding it by using Python f-strings ([_formatted string literals_](https://docs.python.org/3/tutorial/inputoutput.html#formatted-string-literals)).\n", "\n", "
Hints\n", "\n", @@ -914,6 +914,25 @@ "" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of the usage of formatting f-strings [_formatted string literals_](https://docs.python.org/3/tutorial/inputoutput.html#formatted-string-literals):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_float_variable = 3.14159\n", + "my_datetime_variable = pd.Timestamp(\"2022-12-06\")\n", + "print(f\"Adjust the rounding of a float, {my_float_variable} vs {my_float_variable:.2f}\")\n", + "print(f\"Adjust the rounding of a timestamp, {my_datetime_variable} vs {my_datetime_variable:%Y-%m-%d}.\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1050,7 +1069,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/visualization_02_plotnine.ipynb b/_solved/visualization_02_plotnine.ipynb index 74a0591..c20e83b 100644 --- a/_solved/visualization_02_plotnine.ipynb +++ b/_solved/visualization_02_plotnine.ipynb @@ -6,7 +6,7 @@ "source": [ "

Plotnine: Introduction

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -871,7 +871,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/visualization_02_seaborn.ipynb b/_solved/visualization_02_seaborn.ipynb index 129f8dc..5d65438 100644 --- a/_solved/visualization_02_seaborn.ipynb +++ b/_solved/visualization_02_seaborn.ipynb @@ -7,7 +7,7 @@ "

Visualisation: Seaborn

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -489,51 +489,6 @@ "" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary statistics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Aggregations such as `count`, `mean` are embedded in Seaborn (similar to other 'Grammar of Graphics' packages such as ggplot in R and plotnine/altair in Python). We can do these operations directly on the original `titanic` data set in a single coding step:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "sns.catplot(data=titanic, x=\"Survived\", col=\"Pclass\", \n", - " kind=\"count\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use another statistical function to apply on each of the groups, use the `estimator`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sns.catplot(data=titanic, x=\"Sex\", y=\"Age\", col=\"Pclass\", kind=\"bar\", \n", - " estimator=np.mean)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -640,6 +595,53 @@ "sns.despine(left=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summary statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aggregations such as `count`, `mean` are embedded in Seaborn (similar to other 'Grammar of Graphics' packages such as ggplot in R and plotnine/altair in Python). We can do these operations directly on the original `titanic` data set in a single coding step:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "sns.catplot(data=titanic, x=\"Survived\", col=\"Pclass\", \n", + " kind=\"count\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use another statistical function to apply on each of the groups, use the `estimator`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sns.catplot(data=titanic, x=\"Sex\", y=\"Age\", col=\"Pclass\", kind=\"bar\", \n", + " estimator=np.mean)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -997,7 +999,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "nbtutor-solution" + ] + }, "outputs": [], "source": [ "casualties_motorway_trucks = casualties[\n", @@ -1009,7 +1015,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "nbtutor-solution" + ] + }, "outputs": [], "source": [ "sns.catplot(data=casualties_motorway_trucks,\n", @@ -1512,7 +1522,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/_solved/visualization_03_landscape.ipynb b/_solved/visualization_03_landscape.ipynb index 05ac5b1..b55c9f4 100644 --- a/_solved/visualization_03_landscape.ipynb +++ b/_solved/visualization_03_landscape.ipynb @@ -6,7 +6,7 @@ "source": [ "

Visualization - Python's Visualization Landscape

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -24,15 +24,15 @@ "\n", "```\n", "conda install -c conda-forge bokeh plotly altair hvplot holoviews\n", + "conda update -c conda-forge panel\n", "``` \n", "\n", - "To have support of Bokeh inside the Jupyter Lab environment\n", + "To have support of plotly inside the Jupyter Lab environment\n", "```\n", - "conda install jupyter_bokeh\n", + "conda install \"ipywidgets>=7.6\" \n", + "conda install -c conda-forge -c plotly jupyter-dash \n", "```\n", "\n", - "To have support of plotly inside the Jupyter Lab environment everything should be included as suc. However, when seeing only blank/empty space as output in the Jupyter lab environment. Close the Jupyter lab, run the command `jupyter lab build` from the command line and restart the Jupyter lab environment.\n", - "\n", "To run the large data set section, additional package installations are required:\n", "\n", "```\n", @@ -139,7 +139,7 @@ " g = sns.catplot(data=titanic, \n", " x=\"Pclass\", y=\"Survived\", \n", " kind=\"bar\", estimator=np.mean,\n", - " ci=None, color=\"grey\")\n", + " errorbar=None, color=\"grey\")\n", " \n", " g.set_axis_labels(\"Cabin class\", \"Survival rate\")" ] @@ -700,7 +700,7 @@ "source": [ "import hvplot.pandas\n", "\n", - "flow_data.hvplot()" + "flow_data.plot()" ] }, { @@ -812,8 +812,7 @@ "# plotly express does not provide the count statistics out of the box, so calculating these\n", "survival_rate = titanic.groupby(\"Pclass\")['Survived'].mean().reset_index() \n", "\n", - "fig = px.bar(survival_rate, x=\"Pclass\", y=\"Survived\")\n", - "fig.show()" + "px.bar(survival_rate, x=\"Pclass\", y=\"Survived\")" ] }, { @@ -1188,7 +1187,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/00-jupyter_introduction.ipynb b/notebooks/00-jupyter_introduction.ipynb index 8c939cb..bb5fd85 100644 --- a/notebooks/00-jupyter_introduction.ipynb +++ b/notebooks/00-jupyter_introduction.ipynb @@ -6,7 +6,7 @@ "source": [ "

Jupyter notebook INTRODUCTION

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -186,20 +186,6 @@ "#### subsubsection" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Raw Text" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Cfr. any text editor" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -554,7 +540,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/notebooks/_solutions/case3_bacterial_resistance_lab_experiment10.py b/notebooks/_solutions/case3_bacterial_resistance_lab_experiment10.py index e179e90..800fc88 100644 --- a/notebooks/_solutions/case3_bacterial_resistance_lab_experiment10.py +++ b/notebooks/_solutions/case3_bacterial_resistance_lab_experiment10.py @@ -2,6 +2,9 @@ x='Bacterial_genotype', y='log10 Mc', row="Phage", - join=False, ci=None, + join=False, + errorbar=None, + row_order=["Lambda", "T4", "T7"], + order=['WT', 'MUT', 'D87G', 'S83L', 'D516G', 'S512F', 'K43N', 'K88R', 'RSF1010', 'RP4'], aspect=3, height=3, color="black") \ No newline at end of file diff --git a/notebooks/_solutions/pandas_01_data_structures5.py b/notebooks/_solutions/pandas_01_data_structures5.py index afc218b..8c609f9 100644 --- a/notebooks/_solutions/pandas_01_data_structures5.py +++ b/notebooks/_solutions/pandas_01_data_structures5.py @@ -1 +1 @@ -df['Fare'].plot(kind='box') # or .plot.box() \ No newline at end of file +df['Fare'].plot.box() # or .plot(kind='box') \ No newline at end of file diff --git a/notebooks/_solutions/pandas_02_basic_operations2.py b/notebooks/_solutions/pandas_02_basic_operations2.py index 0a4d54d..e00ef91 100644 --- a/notebooks/_solutions/pandas_02_basic_operations2.py +++ b/notebooks/_solutions/pandas_02_basic_operations2.py @@ -1 +1 @@ -df['Age'].hist() #bins=30, log=True \ No newline at end of file +df['Age'].plot.hist() # bins=30, log=True) \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data13.py b/notebooks/_solutions/pandas_03a_selecting_data13.py index 0673ed7..f95fd96 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data13.py +++ b/notebooks/_solutions/pandas_03a_selecting_data13.py @@ -1 +1 @@ -len(titles[titles['title'] == 'Hamlet']) \ No newline at end of file +titles.nsmallest(2, columns="year") \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data14.py b/notebooks/_solutions/pandas_03a_selecting_data14.py index d5b91f8..0673ed7 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data14.py +++ b/notebooks/_solutions/pandas_03a_selecting_data14.py @@ -1 +1 @@ -titles[titles['title'] == 'Treasure Island'].sort_values('year') \ No newline at end of file +len(titles[titles['title'] == 'Hamlet']) \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data15.py b/notebooks/_solutions/pandas_03a_selecting_data15.py index 6a90954..d5b91f8 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data15.py +++ b/notebooks/_solutions/pandas_03a_selecting_data15.py @@ -1 +1 @@ -len(titles[(titles['year'] >= 1950) & (titles['year'] <= 1959)]) \ No newline at end of file +titles[titles['title'] == 'Treasure Island'].sort_values('year') \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data16.py b/notebooks/_solutions/pandas_03a_selecting_data16.py index 4736084..6a90954 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data16.py +++ b/notebooks/_solutions/pandas_03a_selecting_data16.py @@ -1 +1 @@ -len(titles[titles['year'] // 10 == 195]) \ No newline at end of file +len(titles[(titles['year'] >= 1950) & (titles['year'] <= 1959)]) \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data17.py b/notebooks/_solutions/pandas_03a_selecting_data17.py index 81e1340..4736084 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data17.py +++ b/notebooks/_solutions/pandas_03a_selecting_data17.py @@ -1 +1 @@ -inception = cast[cast['title'] == 'Inception'] \ No newline at end of file +len(titles[titles['year'] // 10 == 195]) \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data18.py b/notebooks/_solutions/pandas_03a_selecting_data18.py index 0326615..81e1340 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data18.py +++ b/notebooks/_solutions/pandas_03a_selecting_data18.py @@ -1 +1 @@ -len(inception[inception['n'].isna()]) \ No newline at end of file +inception = cast[cast['title'] == 'Inception'] \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data19.py b/notebooks/_solutions/pandas_03a_selecting_data19.py index 59d4b10..0326615 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data19.py +++ b/notebooks/_solutions/pandas_03a_selecting_data19.py @@ -1 +1 @@ -inception['n'].isna().sum() \ No newline at end of file +len(inception[inception['n'].isna()]) \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data20.py b/notebooks/_solutions/pandas_03a_selecting_data20.py index 8f8f362..59d4b10 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data20.py +++ b/notebooks/_solutions/pandas_03a_selecting_data20.py @@ -1 +1 @@ -len(inception[inception['n'].notna()]) \ No newline at end of file +inception['n'].isna().sum() \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data21.py b/notebooks/_solutions/pandas_03a_selecting_data21.py index 03b0185..8f8f362 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data21.py +++ b/notebooks/_solutions/pandas_03a_selecting_data21.py @@ -1,3 +1 @@ -titanic = cast[(cast['title'] == 'Titanic') & (cast['year'] == 1997)] -titanic = titanic[titanic['n'].notna()] -titanic.sort_values('n') \ No newline at end of file +len(inception[inception['n'].notna()]) \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data22.py b/notebooks/_solutions/pandas_03a_selecting_data22.py index 255c57d..03b0185 100644 --- a/notebooks/_solutions/pandas_03a_selecting_data22.py +++ b/notebooks/_solutions/pandas_03a_selecting_data22.py @@ -1,4 +1,3 @@ -brad = cast[cast['name'] == 'Brad Pitt'] -brad = brad[brad['year'] // 10 == 199] -brad = brad[brad['n'] == 2] -brad.sort_values('year') \ No newline at end of file +titanic = cast[(cast['title'] == 'Titanic') & (cast['year'] == 1997)] +titanic = titanic[titanic['n'].notna()] +titanic.sort_values('n') \ No newline at end of file diff --git a/notebooks/_solutions/pandas_03a_selecting_data23.py b/notebooks/_solutions/pandas_03a_selecting_data23.py new file mode 100644 index 0000000..255c57d --- /dev/null +++ b/notebooks/_solutions/pandas_03a_selecting_data23.py @@ -0,0 +1,4 @@ +brad = cast[cast['name'] == 'Brad Pitt'] +brad = brad[brad['year'] // 10 == 199] +brad = brad[brad['n'] == 2] +brad.sort_values('year') \ No newline at end of file diff --git a/notebooks/_solutions/pandas_05_groupby_operations10.py b/notebooks/_solutions/pandas_05_groupby_operations10.py index 3ca7645..3a89e4e 100644 --- a/notebooks/_solutions/pandas_05_groupby_operations10.py +++ b/notebooks/_solutions/pandas_05_groupby_operations10.py @@ -1,3 +1,3 @@ titles['decade'] = titles['year'] // 10 * 10 hamlet = titles[titles['title'].str.contains('Hamlet')] -hamlet.groupby('decade').size().plot(kind='bar', color="lightblue") \ No newline at end of file +hamlet.groupby('decade').size().plot.bar(color="lightblue") \ No newline at end of file diff --git a/notebooks/_solutions/pandas_05_groupby_operations5.py b/notebooks/_solutions/pandas_05_groupby_operations5.py index 2c2f5ab..d65f0a7 100644 --- a/notebooks/_solutions/pandas_05_groupby_operations5.py +++ b/notebooks/_solutions/pandas_05_groupby_operations5.py @@ -1 +1 @@ -df.groupby('Pclass')['Survived'].mean().plot(kind='bar') #and what if you would compare the total number of survivors? \ No newline at end of file +df.groupby('Pclass')['Survived'].mean().plot.bar() #and what if you would compare the total number of survivors? \ No newline at end of file diff --git a/notebooks/_solutions/pandas_05_groupby_operations6.py b/notebooks/_solutions/pandas_05_groupby_operations6.py index b5a0af0..94e9109 100644 --- a/notebooks/_solutions/pandas_05_groupby_operations6.py +++ b/notebooks/_solutions/pandas_05_groupby_operations6.py @@ -1 +1 @@ -df.groupby('AgeClass')['Fare'].mean().plot(kind='bar', rot=0) \ No newline at end of file +df.groupby('AgeClass')['Fare'].mean().plot.bar(rot=0) \ No newline at end of file diff --git a/notebooks/_solutions/pandas_05_groupby_operations8.py b/notebooks/_solutions/pandas_05_groupby_operations8.py index 700b3d2..c6d147f 100644 --- a/notebooks/_solutions/pandas_05_groupby_operations8.py +++ b/notebooks/_solutions/pandas_05_groupby_operations8.py @@ -1 +1 @@ -titles.groupby('decade').size().plot(kind='bar', color='green') \ No newline at end of file +titles.groupby('decade').size().plot.bar(color='green') \ No newline at end of file diff --git a/notebooks/_solutions/pandas_05_groupby_operations9.py b/notebooks/_solutions/pandas_05_groupby_operations9.py index ce379c3..d23d985 100644 --- a/notebooks/_solutions/pandas_05_groupby_operations9.py +++ b/notebooks/_solutions/pandas_05_groupby_operations9.py @@ -1,3 +1,3 @@ titles['decade'] = titles['year'] // 10 * 10 hamlet = titles[titles['title'] == 'Hamlet'] -hamlet.groupby('decade').size().plot(kind='bar', color="orange") \ No newline at end of file +hamlet.groupby('decade').size().plot.bar(color="orange") \ No newline at end of file diff --git a/notebooks/_solutions/pandas_08_reshaping_data15.py b/notebooks/_solutions/pandas_08_reshaping_data15.py index cea76b8..b60e49b 100644 --- a/notebooks/_solutions/pandas_08_reshaping_data15.py +++ b/notebooks/_solutions/pandas_08_reshaping_data15.py @@ -1,2 +1,3 @@ cast.pivot_table(index='year', columns='type', values="character", aggfunc='count').plot() -# for values in using the , take a column with no Nan values in order to count effectively all values -> at this stage: aha-erlebnis about crosstab function(!) \ No newline at end of file +# for the values column to use in the aggfunc, take a column with no NaN values in order to count effectively all values +# -> at this stage: aha-erlebnis about crosstab function(!) \ No newline at end of file diff --git a/notebooks/_solutions/pandas_08_reshaping_data17.py b/notebooks/_solutions/pandas_08_reshaping_data17.py index 6b80b38..f3744d0 100644 --- a/notebooks/_solutions/pandas_08_reshaping_data17.py +++ b/notebooks/_solutions/pandas_08_reshaping_data17.py @@ -1 +1 @@ -pd.crosstab(index=cast['year'], columns=cast['type']).plot(kind='area') \ No newline at end of file +pd.crosstab(index=cast['year'], columns=cast['type']).plot.area() \ No newline at end of file diff --git a/notebooks/_solutions/pandas_08_reshaping_data5.py b/notebooks/_solutions/pandas_08_reshaping_data5.py index ff73f0e..9608d65 100644 --- a/notebooks/_solutions/pandas_08_reshaping_data5.py +++ b/notebooks/_solutions/pandas_08_reshaping_data5.py @@ -1,2 +1,2 @@ -df_overall = df_tidy.groupby(["time", "Energie"]).sum() # or with .reset_index() +df_overall = df_tidy.groupby(["time", "Energie"])[["consumption"]].sum() # or with .reset_index() df_overall.head() \ No newline at end of file diff --git a/notebooks/_solutions/pandas_08_reshaping_data8.py b/notebooks/_solutions/pandas_08_reshaping_data8.py index 70ee693..4f7eea7 100644 --- a/notebooks/_solutions/pandas_08_reshaping_data8.py +++ b/notebooks/_solutions/pandas_08_reshaping_data8.py @@ -1,6 +1,6 @@ fig, ax1 = plt.subplots() -df.pivot_table(index='Pclass', columns='Sex', - values='Survived', aggfunc='mean').plot(kind='bar', - rot=0, - ax=ax1) +(df.pivot_table(index='Pclass', columns='Sex', + values='Survived', aggfunc='mean') + .plot.bar(rot=0, ax=ax1) +) ax1.set_ylabel('Survival ratio') \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn10.py b/notebooks/_solutions/visualization_02_seaborn10.py index 92a2dcc..eaa9d15 100644 --- a/notebooks/_solutions/visualization_02_seaborn10.py +++ b/notebooks/_solutions/visualization_02_seaborn10.py @@ -1,3 +1,11 @@ -monthly_victim_counts = casualties.resample("M", on="datetime")[ - ["n_victims_ok", "n_slightly_injured", "n_seriously_injured", "n_dead_30days"] -].sum() \ No newline at end of file +# filter the data +compare_dead_30 = casualties.set_index("datetime")["2019":"2021"] +compare_dead_30 = compare_dead_30[compare_dead_30["road_user_type"].isin( + ["Bicycle", "Passenger car", "Pedestrian", "Motorbike"])] + +# Sum the victims and dead within 30 days victims for each year/road-user type combination +compare_dead_30 = compare_dead_30.groupby( + ["road_user_type", compare_dead_30.index.year])[["n_dead_30days", "n_victims"]].sum().reset_index() + +# create a new colum with the percentage deads +compare_dead_30["dead_prop"] = compare_dead_30["n_dead_30days"] / compare_dead_30["n_victims"] * 100 \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn11.py b/notebooks/_solutions/visualization_02_seaborn11.py index 2ac5c8f..1d8a7e9 100644 --- a/notebooks/_solutions/visualization_02_seaborn11.py +++ b/notebooks/_solutions/visualization_02_seaborn11.py @@ -1,6 +1,6 @@ -sns.relplot( - data=monthly_victim_counts, - kind="line", - palette="colorblind", - height=3, aspect=4, -) \ No newline at end of file +sns.catplot(data=compare_dead_30, + x="dead_prop", + y="road_user_type", + kind="bar", + hue="datetime" + ) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn12.py b/notebooks/_solutions/visualization_02_seaborn12.py index b4c44cb..92a2dcc 100644 --- a/notebooks/_solutions/visualization_02_seaborn12.py +++ b/notebooks/_solutions/visualization_02_seaborn12.py @@ -1,14 +1,3 @@ -# Optional solution with tidy data representation (providing x and y) -monthly_victim_counts_melt = monthly_victim_counts.reset_index().melt( - id_vars="datetime", var_name="victim_type", value_name="count" -) - -sns.relplot( - data=monthly_victim_counts_melt, - x="datetime", - y="count", - hue="victim_type", - kind="line", - palette="colorblind", - height=3, aspect=4, -) \ No newline at end of file +monthly_victim_counts = casualties.resample("M", on="datetime")[ + ["n_victims_ok", "n_slightly_injured", "n_seriously_injured", "n_dead_30days"] +].sum() \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn13.py b/notebooks/_solutions/visualization_02_seaborn13.py index 91f138a..2ac5c8f 100644 --- a/notebooks/_solutions/visualization_02_seaborn13.py +++ b/notebooks/_solutions/visualization_02_seaborn13.py @@ -1,2 +1,6 @@ -# Pandas area plot -monthly_victim_counts.plot.area(colormap='Reds', figsize=(15, 5)) \ No newline at end of file +sns.relplot( + data=monthly_victim_counts, + kind="line", + palette="colorblind", + height=3, aspect=4, +) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn14.py b/notebooks/_solutions/visualization_02_seaborn14.py index f9b66ba..b4c44cb 100644 --- a/notebooks/_solutions/visualization_02_seaborn14.py +++ b/notebooks/_solutions/visualization_02_seaborn14.py @@ -1,3 +1,14 @@ -# Using Pandas -daily_total_counts_2020 = casualties.set_index("datetime")["2020":"2021"].resample("D")["n_victims"].sum() -daily_total_counts_2020.plot.line(figsize=(12, 3)) \ No newline at end of file +# Optional solution with tidy data representation (providing x and y) +monthly_victim_counts_melt = monthly_victim_counts.reset_index().melt( + id_vars="datetime", var_name="victim_type", value_name="count" +) + +sns.relplot( + data=monthly_victim_counts_melt, + x="datetime", + y="count", + hue="victim_type", + kind="line", + palette="colorblind", + height=3, aspect=4, +) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn15.py b/notebooks/_solutions/visualization_02_seaborn15.py index 338e810..91f138a 100644 --- a/notebooks/_solutions/visualization_02_seaborn15.py +++ b/notebooks/_solutions/visualization_02_seaborn15.py @@ -1,4 +1,2 @@ -# Using Seaborn -sns.relplot(data=daily_total_counts_2020, - kind="line", - aspect=4, height=3) \ No newline at end of file +# Pandas area plot +monthly_victim_counts.plot.area(colormap='Reds', figsize=(15, 5)) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn16.py b/notebooks/_solutions/visualization_02_seaborn16.py index 4ea8857..f9b66ba 100644 --- a/notebooks/_solutions/visualization_02_seaborn16.py +++ b/notebooks/_solutions/visualization_02_seaborn16.py @@ -1,19 +1,3 @@ -# weekly proportion of deadly victims for each light condition -weekly_victim_dead_lc = ( - casualties - .groupby("light_conditions") - .resample("W", on="datetime")[["datetime", "n_victims", "n_dead_30days"]] - .sum() - .reset_index() - ) -weekly_victim_dead_lc["dead_prop"] = weekly_victim_dead_lc["n_dead_30days"] / weekly_victim_dead_lc["n_victims"] * 100 - -# .. and the same for each road type -weekly_victim_dead_rt = ( - casualties - .groupby("road_type") - .resample("W", on="datetime")[["datetime", "n_victims", "n_dead_30days"]] - .sum() - .reset_index() -) -weekly_victim_dead_rt["dead_prop"] = weekly_victim_dead_rt["n_dead_30days"] / weekly_victim_dead_rt["n_victims"] * 100 \ No newline at end of file +# Using Pandas +daily_total_counts_2020 = casualties.set_index("datetime")["2020":"2021"].resample("D")["n_victims"].sum() +daily_total_counts_2020.plot.line(figsize=(12, 3)) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn17.py b/notebooks/_solutions/visualization_02_seaborn17.py index 7966027..338e810 100644 --- a/notebooks/_solutions/visualization_02_seaborn17.py +++ b/notebooks/_solutions/visualization_02_seaborn17.py @@ -1,4 +1,4 @@ -fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 5)) - -sns.ecdfplot(data=weekly_victim_dead_lc, x="dead_prop", hue="light_conditions", ax=ax0) -sns.ecdfplot(data=weekly_victim_dead_rt, x="dead_prop", hue="road_type", ax=ax1) \ No newline at end of file +# Using Seaborn +sns.relplot(data=daily_total_counts_2020, + kind="line", + aspect=4, height=3) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn18.py b/notebooks/_solutions/visualization_02_seaborn18.py index a8391c2..4ea8857 100644 --- a/notebooks/_solutions/visualization_02_seaborn18.py +++ b/notebooks/_solutions/visualization_02_seaborn18.py @@ -1,2 +1,19 @@ -daily_min_temp_2020 = pd.read_csv("./data/daily_min_temperature_2020.csv", - parse_dates=["datetime"]) \ No newline at end of file +# weekly proportion of deadly victims for each light condition +weekly_victim_dead_lc = ( + casualties + .groupby("light_conditions") + .resample("W", on="datetime")[["datetime", "n_victims", "n_dead_30days"]] + .sum() + .reset_index() + ) +weekly_victim_dead_lc["dead_prop"] = weekly_victim_dead_lc["n_dead_30days"] / weekly_victim_dead_lc["n_victims"] * 100 + +# .. and the same for each road type +weekly_victim_dead_rt = ( + casualties + .groupby("road_type") + .resample("W", on="datetime")[["datetime", "n_victims", "n_dead_30days"]] + .sum() + .reset_index() +) +weekly_victim_dead_rt["dead_prop"] = weekly_victim_dead_rt["n_dead_30days"] / weekly_victim_dead_rt["n_victims"] * 100 \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn19.py b/notebooks/_solutions/visualization_02_seaborn19.py index 7d82dad..7966027 100644 --- a/notebooks/_solutions/visualization_02_seaborn19.py +++ b/notebooks/_solutions/visualization_02_seaborn19.py @@ -1 +1,4 @@ -daily_with_temp = daily_total_counts_2020.reset_index().merge(daily_min_temp_2020, on="datetime") \ No newline at end of file +fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 5)) + +sns.ecdfplot(data=weekly_victim_dead_lc, x="dead_prop", hue="light_conditions", ax=ax0) +sns.ecdfplot(data=weekly_victim_dead_rt, x="dead_prop", hue="road_type", ax=ax1) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn20.py b/notebooks/_solutions/visualization_02_seaborn20.py index 29e1f47..a8391c2 100644 --- a/notebooks/_solutions/visualization_02_seaborn20.py +++ b/notebooks/_solutions/visualization_02_seaborn20.py @@ -1,3 +1,2 @@ -g = sns.jointplot( - data=daily_with_temp, x="air_temperature", y="n_victims", kind="reg" -) \ No newline at end of file +daily_min_temp_2020 = pd.read_csv("./data/daily_min_temperature_2020.csv", + parse_dates=["datetime"]) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn21.py b/notebooks/_solutions/visualization_02_seaborn21.py new file mode 100644 index 0000000..7d82dad --- /dev/null +++ b/notebooks/_solutions/visualization_02_seaborn21.py @@ -0,0 +1 @@ +daily_with_temp = daily_total_counts_2020.reset_index().merge(daily_min_temp_2020, on="datetime") \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn22.py b/notebooks/_solutions/visualization_02_seaborn22.py new file mode 100644 index 0000000..29e1f47 --- /dev/null +++ b/notebooks/_solutions/visualization_02_seaborn22.py @@ -0,0 +1,3 @@ +g = sns.jointplot( + data=daily_with_temp, x="air_temperature", y="n_victims", kind="reg" +) \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn8.py b/notebooks/_solutions/visualization_02_seaborn8.py index eaa9d15..390d344 100644 --- a/notebooks/_solutions/visualization_02_seaborn8.py +++ b/notebooks/_solutions/visualization_02_seaborn8.py @@ -1,11 +1,4 @@ -# filter the data -compare_dead_30 = casualties.set_index("datetime")["2019":"2021"] -compare_dead_30 = compare_dead_30[compare_dead_30["road_user_type"].isin( - ["Bicycle", "Passenger car", "Pedestrian", "Motorbike"])] - -# Sum the victims and dead within 30 days victims for each year/road-user type combination -compare_dead_30 = compare_dead_30.groupby( - ["road_user_type", compare_dead_30.index.year])[["n_dead_30days", "n_victims"]].sum().reset_index() - -# create a new colum with the percentage deads -compare_dead_30["dead_prop"] = compare_dead_30["n_dead_30days"] / compare_dead_30["n_victims"] * 100 \ No newline at end of file +casualties_motorway_trucks = casualties[ + (casualties["road_type"] == "Motorway") + & casualties["road_user_type"].isin(["Light truck", "Truck"]) +] \ No newline at end of file diff --git a/notebooks/_solutions/visualization_02_seaborn9.py b/notebooks/_solutions/visualization_02_seaborn9.py index 1d8a7e9..4bea747 100644 --- a/notebooks/_solutions/visualization_02_seaborn9.py +++ b/notebooks/_solutions/visualization_02_seaborn9.py @@ -1,6 +1,9 @@ -sns.catplot(data=compare_dead_30, - x="dead_prop", - y="road_user_type", +sns.catplot(data=casualties_motorway_trucks, + x="week_day", + y="n_victims", + estimator=np.sum, + ci=None, kind="bar", - hue="datetime" - ) \ No newline at end of file + color="#900C3F", + height=3, + aspect=4) \ No newline at end of file diff --git a/notebooks/case1_bike_count.ipynb b/notebooks/case1_bike_count.ipynb index 0134268..8f93dc1 100644 --- a/notebooks/case1_bike_count.ipynb +++ b/notebooks/case1_bike_count.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - Bike count data

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -187,8 +187,8 @@ "\n", "Pre-process the data:\n", "\n", - "* Combine the 'datum' and 'tijd' columns into one Pandas Series of string datetime values, call this new variable `combined`.\n", - "* Parse the string datetime values to `datetime` objects.\n", + "* Combine the 'datum' and 'tijd' columns into one Pandas Series of string values, call this new variable `combined`.\n", + "* Parse the string values to `datetime` objects.\n", "* Set the resulting `datetime` column as the index of the `df` DataFrame.\n", "* Remove the original 'datum' and 'tijd' columns using the `drop` method, and call the new dataframe `df2`.\n", "* Rename the columns in the DataFrame 'ri Centrum', 'ri Mariakerke' to resp. 'direction_centre', 'direction_mariakerke' using the `rename` method.\n", @@ -1114,7 +1114,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So the daily pattern is clearly different for both directions. In the morning more people go north, in the evening more people go south. The morning peak is also more condensed." + "So the daily pattern is clearly different for both directions. In the morning more people go towards the centre, in the evening more people go back to Mariakerke. The morning peak is also more condensed." ] }, { @@ -1186,7 +1186,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/notebooks/case2_observations.ipynb b/notebooks/case2_observations.ipynb index bd626a3..22153a2 100644 --- a/notebooks/case2_observations.ipynb +++ b/notebooks/case2_observations.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - Observation data

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -304,11 +304,11 @@ "\n", "**EXERCISE 5**\n", "\n", - "How many records in the data set have no information about the `species`? Use the `isna()` method to find out.\n", + "How many records in the data set have no information about the `species_ID`? Use the `isna()` method to find out.\n", "\n", "
Hints\n", "\n", - "- Do NOT use `survey_data_processed['species'] == np.nan`, but use the available method `isna()` to check if a value is NaN\n", + "- Do NOT use `survey_data_processed['species_ID'] == np.nan`, but use the available method `isna()` to check if a value is NaN\n", "- The result of an (element-wise) condition returns a set of True/False values, corresponding to 1/0 values. The amount of True values is equal to the sum.\n", " \n", "
" @@ -1616,7 +1616,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.8.13" }, "nav_menu": {}, "toc": { diff --git a/notebooks/case2_observations_analysis.ipynb b/notebooks/case2_observations_analysis.ipynb deleted file mode 100644 index 1857b11..0000000 --- a/notebooks/case2_observations_analysis.ipynb +++ /dev/null @@ -1,1274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

CASE - Observation data - analysis

\n", - "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", - "\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "plt.style.use('seaborn-whitegrid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Reading in the enriched observations data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 1**\n", - "\n", - "- Read in the `survey_data_completed.csv` file and save the resulting `DataFrame` as variable `survey_data_processed` (if you did not complete the previous notebook, a version of the csv file is available in the `data` folder).\n", - "- Interpret the 'eventDate' column directly as python `datetime` objects and make sure the 'occurrenceID' column is used as the index of the resulting DataFrame (both can be done at once when reading the csv file using parameters of the `read_csv` function)\n", - "- Inspect the first five rows of the DataFrame and the data types of each of the data columns. Verify that the 'eventDate' indeed has a datetime data type.\n", - "\n", - "
Hints\n", - "\n", - "- All read functions in Pandas start with `pd.read_...`.\n", - "- To check the documentation of a function, use the keystroke combination of SHIFT + TAB when the cursor is on the function.\n", - "- Remember `.head()` and `.info()`?\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis1.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis2.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis3.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Tackle missing values (NaN) and duplicate values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See [pandas_07_missing_values.ipynb](pandas_07_missing_values.ipynb) for an overview of functionality to work with missing values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 2**\n", - "\n", - "How many records in the data set have no information about the `species`? Use the `isna()` method to find out.\n", - "\n", - "
Hints\n", - "\n", - "- Do NOT use `survey_data_processed['species'] == np.nan`, but use the available method `isna()` to check if a value is NaN\n", - "- The result of an (element-wise) condition returns a set of True/False values, corresponding to 1/0 values. The amount of True values is equal to the sum.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis4.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 3**\n", - "\n", - "How many duplicate records are present in the dataset? Use the method `duplicated()` to check if a row is a duplicate.\n", - "\n", - "
Hints\n", - "\n", - "- The result of an (element-wise) condition returns a set of True/False values, corresponding to 1/0 values. The amount of True values is equal to the sum.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis5.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 4**\n", - "\n", - "- Select all duplicate data by filtering the `observations` data and assign the result to a new variable `duplicate_observations`. The `duplicated()` method provides a `keep` argument define which duplicates (if any) to mark.\n", - "- Sort the `duplicate_observations` data on both the columns `eventDate` and `verbatimLocality` and show the first 9 records.\n", - "\n", - "
Hints\n", - "\n", - "- Check the documentation of the `duplicated` method to find out which value the argument `keep` requires to select all duplicate data.\n", - "- `sort_values()` can work with a single columns name as well as a list of names.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis6.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 5**\n", - "\n", - "- Exclude the duplicate values (i.e. keep the first occurrence while removing the other ones) from the `observations` data set and save the result as `survey_data_unique`. Use the `drop duplicates()` method from Pandas.\n", - "- How many observations are still left in the data set?\n", - "\n", - "
Hints\n", - "\n", - "- `keep=First` is the default option for `drop_duplicates`\n", - "- The number of rows in a DataFrame is equal to the `len`gth\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis7.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis8.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 6**\n", - "\n", - "Use the `dropna()` method to find out:\n", - "\n", - "- For how many observations (rows) we have all the information available (i.e. no NaN values in any of the columns)?\n", - "- For how many observations (rows) we do have the `species` data available ?\n", - "\n", - "
Hints\n", - "\n", - "- `dropna` by default removes by default all rows for which _any_ of the columns contains a `NaN` value.\n", - "- To specify which specific columns to check, use the `subset` argument\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis9.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 7**\n", - "\n", - "Filter the `survey_data_unique` data and select only those records that do not have a `species` while having information on the `sex`. Store the result as variable `not_identified`.\n", - "\n", - "
Hints\n", - "\n", - "- To combine logical operators element-wise in Pandas, use the `&` operator.\n", - "- Pandas provides both a `isna()` and a `notna()` method to check the existence of `NaN` values.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis10.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "not_identified.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__NOTE!__\n", - "\n", - "The `DataFrame` we will use in the further analyses contains species information:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data = survey_data_unique.dropna(subset=['species']).copy()\n", - "survey_data['name'] = survey_data['genus'] + ' ' + survey_data['species']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**INFO**\n", - "\n", - "For biodiversity studies, absence values (knowing that something is not present) are useful as well to normalize the observations, but this is out of scope for these exercises.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Select subsets of the data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data['taxa'].value_counts()\n", - "#survey_data.groupby('taxa').size()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 8**\n", - "\n", - "- Select the observations for which the `taxa` is equal to 'Rabbit', 'Bird' or 'Reptile'. Assign the result to a variable `non_rodent_species`. Use the `isin` method for the selection.\n", - "\n", - "
Hints\n", - "\n", - "- You do not have to combine three different conditions, but use the `isin` operator with a list of names.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis11.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(non_rodent_species)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 9**\n", - "\n", - "Select the observations for which the `name` starts with the characters 'r' (make sure it does not matter if a capital character is used in the 'taxa' name). Call the resulting variable `r_species`.\n", - "\n", - "
Hints\n", - "\n", - "- Remember the `.str.` construction to provide all kind of string functionalities? You can combine multiple of these after each other.\n", - "- If the presence of capital letters should not matter, make everything lowercase first before comparing (`.lower()`)\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis12.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(r_species)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "r_species[\"name\"].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 10**\n", - "\n", - "Select the observations that are not Birds. Call the resulting variable non_bird_species.\n", - "\n", - "
Hints\n", - "\n", - "- Logical operators like `==`, `!=`, `>`,... can still be used.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis13.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(non_bird_species)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 11**\n", - "\n", - "Select the __Bird__ (taxa is Bird) observations from 1985-01 till 1989-12 using the `eventDate` column. Call the resulting variable `birds_85_89`.\n", - "\n", - "
Hints\n", - "\n", - "- No hints, you can do this! (with the help of some `<=` and `&`, and don't forget the put brackets around each comparison that you combine)\n", - "\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis14.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternative solution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis15.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 12**\n", - "\n", - "- Drop the observations for which no 'weight' (`wgt` column) information is available.\n", - "- On the filtered data, compare the median weight for each of the species (use the `name` column)\n", - "- Sort the output from high to low median weight (i.e. descending)\n", - "\n", - "__Note__ You can do this all in a single line statement, but don't have to do it as such!\n", - "\n", - "
Hints\n", - "\n", - "- You will need `dropna`, `groupby`, `median` and `sort_values`.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis16.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis17.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Species abundance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 13**\n", - "\n", - "Which 8 species (use the `name` column to identify the different species) have been observed most over the entire data set?\n", - "\n", - "
Hints\n", - "\n", - "- Pandas provide a function to combine sorting and showing the first n records, see [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.nlargest.html)...\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis18.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis19.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 14**\n", - "\n", - "- What is the number of different species (`name`) in each of the `verbatimLocality` plots? Use the `nunique` method. Assign the output to a new variable `n_species_per_plot`.\n", - "- Define a Matplotlib `Figure` (`fig`) and `Axes` (`ax`) to prepare a plot. Make an horizontal bar chart using Pandas `plot` function linked to the just created Matplotlib `ax`. Each bar represents the `species per plot/verbatimLocality`. Change the y-label to 'Plot number'.\n", - "\n", - "
Hints\n", - "\n", - "- _...in each of the..._ should provide a hint to use `groupby` for this exercise. The `nunique` is the aggregation function for each of the groups.\n", - "- `fig, ax = plt.subplots()` prepares a Matplotlib Figure and Axes.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis20.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 15**\n", - "\n", - "- What is the number of plots (`verbatimLocality`) each of the species (`name`) have been observed in? Assign the output to a new variable `n_plots_per_species`. Sort the counts from low to high.\n", - "- Make an horizontal bar chart using Pandas `plot` function to show the number of plots each of the species was found (using the `n_plots_per_species` variable).\n", - "\n", - "
Hints\n", - "\n", - "- Use the previous exercise to solve this one.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis21.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 16**\n", - "\n", - "- Starting from the `survey_data`, calculate the amount of males and females present in each of the plots (`verbatimLocality`). The result should return the counts for each of the combinations of `sex` and `verbatimLocality`. Assign to a new variable `n_plot_sex` and ensure the counts are in a column named \"count\".\n", - "- Use `pivot` to convert the `n_plot_sex` DataFrame to a new DataFrame with the `verbatimLocality` as index and `male`/`female` as column names. Assign to a new variable `pivoted`.\n", - "\n", - "
Hints\n", - "\n", - "- _...for each of the combinations..._ `groupby` can also be used with multiple columns at the same time.\n", - "- If a `groupby` operation gives a Series as result, you can give that Series a name with the `.rename(..)` method.\n", - "- `reset_index()` is useful function to convert multiple indices into columns again.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis22.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis23.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pivoted.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To check, we can use the variable `pivoted` to plot the result:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pivoted.plot(kind='bar', figsize=(12, 6), rot=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 17**\n", - "\n", - "Recreate the previous plot with the `catplot` function from the Seaborn library starting from `n_plot_sex`.\n", - "\n", - "
Hints\n", - "\n", - "- Check the `kind` argument of the `catplot` function to figure out to specify you want a barplot with given x and y values.\n", - "- To link a column to different colors, use the `hue` argument\n", - "- Using `height` and `aspect`, the figure size can be optimized.\n", - "\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis24.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 18**\n", - "\n", - "Recreate the previous plot with the `catplot` function from the Seaborn library directly starting from `survey_data`.\n", - "\n", - "
Hints\n", - "\n", - "- Check the `kind`argument of the `catplot` function to find out how to use counts to define the bars instead of a `y` value.\n", - "- To link a column to different colors, use the `hue` argument\n", - "\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis25.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 19**\n", - "\n", - "- Make a summary table with the number of records of each of the species in each of the plots (also called `verbatimLocality`). Each of the species `name`s is a row index and each of the `verbatimLocality` plots is a column name.\n", - "- Using the Seaborn documentation to make a heatmap.\n", - "\n", - "
Hints\n", - "\n", - "- Make sure to pass the correct columns to respectively the `index`, `columns`, `values` and `aggfunc` parameters of the `pivot_table` function. You can use the `datasetName` to count the number of observations for each name/locality combination (when counting rows, the exact column doesn't matter).\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis26.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis27.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Observations over time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 20**\n", - "\n", - "Make a plot visualizing the evolution of the number of observations for each of the individual __years__ (i.e. annual counts) using the `resample` method.\n", - "\n", - "
Hints\n", - "\n", - "- You want to `resample` the data using the `eventDate` column to create annual counts. If the index is not a datetime-index, you can use the `on=` keyword to specify which datetime column to use.\n", - "- `resample` needs an aggregation function on how to combine the values within a single 'group' (in this case data within a year). In this example, we want to know the `size` of each group, i.e. the number of records within each year.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis28.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To evaluate the intensity or number of occurrences during different time spans, a heatmap is an interesting representation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 21**\n", - "\n", - "- Create a table, called `heatmap_prep`, based on the `survey_data` DataFrame with the row index the individual years, in the column the months of the year (1-> 12) and as values of the table, the counts for each of these year/month combinations.\n", - "- Using the seaborn documentation, make a heatmap starting from the `heatmap_prep` variable.\n", - "\n", - "
Hints\n", - "\n", - "- The `.dt` accessor can be used to get the `year`, `month`,... from a `datetime` column\n", - "- Use `pivot_table` and provide the years to `index` and the months to `columns`. Do not forget to `count` the number for each combination (`aggfunc`).\n", - "- Seaborn has an `heatmap` function which requires a short-form DataFrame, comparable to giving each element in a table a color value.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis29.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remark that we started from a `tidy` data format (also called *long* format) and converted to *short* format with in the row index the years, in the column the months and the counts for each of these year/month combinations as values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## (OPTIONAL SECTION) 6. Evolution of species during monitoring period" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*In this section, all plots can be made with the embedded Pandas plot function, unless specificly asked*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 22**\n", - "\n", - "Plot using Pandas `plot` function the number of records for `Dipodomys merriami` for each month of the year (January (1) -> December (12)), aggregated over all years.\n", - "\n", - "
Hints\n", - "\n", - "- _...for each month of..._ requires `groupby`.\n", - "- `resample` is not useful here, as we do not want to change the time-interval, but look at month of the year (over all years)\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis30.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis31.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 23**\n", - "\n", - "Plot, for the species 'Dipodomys merriami', 'Dipodomys ordii', 'Reithrodontomys megalotis' and 'Chaetodipus baileyi', the monthly number of records as a function of time during the monitoring period. Plot each of the individual species in a separate subplot and provide them all with the same y-axis scale\n", - "\n", - "
Hints\n", - "\n", - "- `isin` is useful to select from within a list of elements.\n", - "- `groupby` AND `resample` need to be combined. We do want to change the time-interval to represent data as a function of time (`resample`) and we want to do this _for each name/species_ (`groupby`). The order matters!\n", - "- `unstack` is a Pandas function a bit similar to `pivot`. Check the [unstack documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.unstack.html) as it might be helpful for this exercise.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis32.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis33.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis34.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 24**\n", - "\n", - "Recreate the same plot as in the previous exercise using Seaborn `relplot` functon with the `month_evolution` variable.\n", - " \n", - "
Hints\n", - "\n", - "- We want to have the `counts` as a function of `eventDate`, so link these columns to y and x respectively.\n", - "- To create subplots in Seaborn, the usage of _facetting_ (splitting data sets to multiple facets) is used by linking a column name to the `row`/`col` parameter. \n", - "- Using `height` and `aspect`, the figure size can be optimized.\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Uncomment the next cell (calculates `month_evolution`, the intermediate result of the previous excercise):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis35.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting with seaborn:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis36.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 25**\n", - "\n", - "Plot the annual amount of occurrences for each of the 'taxa' as a function of time using Seaborn. Plot each taxa in a separate subplot and do not share the y-axis among the facets.\n", - "\n", - "
Hints\n", - "\n", - "- Combine `resample` and `groupby`!\n", - "- Check out the previous exercise for the plot function.\n", - "- Pass the `sharey=False` to the `facet_kws` argument as a dictionary.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis37.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis38.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 26**\n", - "\n", - "The observations where taken by volunteers. You wonder on which day of the week the most observations where done. Calculate for each day of the week (`dayofweek`) the number of observations and make a bar plot.\n", - "\n", - "
Hints\n", - "\n", - "- Did you know the Python standard Library has a module `calendar` which contains names of week days, month names,...?\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_analysis39.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nice work!" - ] - } - ], - "metadata": { - "celltoolbar": "Nbtutor - export exercises", - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - }, - "nav_menu": {}, - "toc": { - "navigate_menu": true, - "number_sections": true, - "sideBar": true, - "threshold": 6, - "toc_cell": false, - "toc_section_display": "block", - "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/case2_observations_processing.ipynb b/notebooks/case2_observations_processing.ipynb deleted file mode 100644 index 1eeb02a..0000000 --- a/notebooks/case2_observations_processing.ipynb +++ /dev/null @@ -1,2229 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

CASE - Observation data - data cleaning and enrichment

\n", - "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", - "\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.style.use('seaborn-whitegrid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Scenario**:
\n", - "\n", - "Observation data of species (when and where is a given species observed) is typical in biodiversity studies. Large international initiatives support the collection of this data by volunteers, e.g. [iNaturalist](https://www.inaturalist.org/). Thanks to initiatives like [GBIF](https://www.gbif.org/), a lot of these data is also openly available." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You decide to share data of a field campaign, but the data set still requires some cleaning and standardization. For example, the coordinates, can be named `x`/`y`, `decimalLatitude`/`decimalLongitude`, `lat`/`long`... Luckily, you know of an international **open data standard** to describe occurrence/observation data, i.e. [Darwin Core (DwC)](http://rs.tdwg.org/dwc/terms). Instead of inventing your own data model, you decide to comply to this international standard. The latter will enhance communication and will also make your data compliant with GBIF." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In short, the DwC describes a flat table (cfr. `CSV`) with an agreed name convention on the header names and conventions on how certain data types need to be represented (as a reference, an in depth description is given [here](https://www.tdwg.org/standards/dwc/)). For this tutorial, we will focus on a few of the existing terms to learn some elements about data cleaning:\n", - "* `eventDate`: ISO 6801 format of dates\n", - "* `scientificName`: the accepted scientific name of the species\n", - "* `decimalLatitude`/`decimalLongitude`: coordinates of the occurrence in WGS84 format\n", - "* `sex`: either `male` or `female` to characterize the sex of the occurrence\n", - "* `occurrenceID`: an identifier within the data set to identify the individual records\n", - "* `datasetName`: a static string defining the source of the data\n", - "\n", - "Furthermore, additional information concerning the taxonomy will be added using an external API service" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Dataset to work on:**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this data set, the data is split up in the following main data files:\n", - "* `surveys.csv` the data with the surveys in the individual plots\n", - "* `species.csv` the overview list of the species short-names\n", - "* `plot_location.xlsx` the overview of coordinates of the individual locations\n", - "\n", - "The data originates from a [study](http://esapubs.org/archive/ecol/E090/118/metadata.htm) of a Chihuahuan desert ecosystem near Portal, Arizona.\n", - "\n", - "![](../img/plot_overview.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Survey-data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Reading in the data of the individual surveys:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data = pd.read_csv(\"data/surveys.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 1**\n", - "\n", - "- How many individual records (occurrences) does the survey data set contain?\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing1.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding the data source information as static column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For convenience when this data-set will be combined with other datasets, we first add a column of static values, defining the `datasetName` of this particular data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datasetname = \"Ecological Archives E090-118-D1.\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adding this static value as a new column `datasetName`:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 2**\n", - "\n", - "Add a new column, `datasetName`, to the survey data set with `datasetname` as value for all of the records (static value for the entire data set)\n", - "\n", - "
Hints\n", - "\n", - "- When a column does not exist, a new `df[\"a_new_column\"]` can be created by assigning a value to it.\n", - "- No `for`-loop is required, as Pandas will automatically broadcast a single string value to each of the rows in the `DataFrame`.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing2.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cleaning the `sex_char` column into a DwC called [sex](http://rs.tdwg.org/dwc/terms/#sex) column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 3**\n", - "\n", - "- Get a list of the unique values for the column `sex_char`.\n", - "\n", - "
Hints\n", - "\n", - "- To find the unique values, look for a function called `unique` (remember `SHIFT`+`TAB` combination to explore the available methods/attributes?)\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing3.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, apparently, more information is provided in this column, whereas according to the [metadata](http://esapubs.org/archive/ecol/E090/118/Portal_rodent_metadata.htm) information, the sex information should be either `M` (male) or `F` (female). We will create a column, named `sex` and convert the symbols to the corresponding sex, taking into account the following mapping of the values (see [metadata](http://esapubs.org/archive/ecol/E090/118/Portal_rodent_metadata.htm) for more details):\n", - "* `M` -> `male`\n", - "* `F` -> `female`\n", - "* `R` -> `male`\n", - "* `P` -> `female`\n", - "* `Z` -> nan\n", - "\n", - "At the same time, we will save the original information of the `sex_char` in a separate column, called `verbatimSex`, as a reference in case we need the original data later." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In summary, we have to:\n", - "* rename the `sex_char` column to `verbatimSex`\n", - "* create a new column with the name `sex`\n", - "* map the original values of the `sex_char` to the values `male` and `female` according to the mapping above" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's convert the name of the column header `sex_char` to `verbatimSex` with the `rename` function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data = survey_data.rename(columns={'sex_char': 'verbatimSex'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 4**\n", - "\n", - "- Express the mapping of the values (e.g. `M` -> `male`) into a Python dictionary object with the variable name `sex_dict`. `Z` values correspond to _Not a Number_, which can be defined as `np.nan`.\n", - "- Use the `sex_dict` dictionary to replace the values in the `verbatimSex` column to the new values and save the mapped values in a new column 'sex' of the DataFrame.\n", - "\n", - "
Hints\n", - "\n", - "- A dictionary is a Python standard library data structure, see https://docs.python.org/3/tutorial/datastructures.html#dictionaries - no Pandas magic involved when you need a key/value mapping.\n", - "- When you need to replace values, look for the Pandas method `replace`.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing4.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing5.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Checking the current frequency of values of the resulting `sex` column (this should result in the values `male`, `female` and `nan`):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data[\"sex\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To check what the frequency of occurrences is for male/female of the categories, a bar chart is a possible representation:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 5**\n", - "\n", - "- Make a horizontal bar chart comparing the number of male, female and unknown (`NaN`) records in the data set.\n", - "\n", - "
Hints\n", - "\n", - "- Pandas provides a shortcut method `value_counts` which works on Pandas `Series` to count unique values. Explore the documentation of the `value_counts` method to include the `NaN` values as well.\n", - "- Check in the help of the Pandas plot function for the `kind` parameter.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing6.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "NOTE: The usage of `groupby` combined with the `size` of each group would be an option as well. However, the latter does not support to count the `NaN` values as well. The `value_counts` method does support this with the `dropna=False` argument.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solving double entry field by decoupling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When checking the species unique information:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data[\"species\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There apparently exists a double entry: `'DM and SH'`, which basically defines two records and should be decoupled to two individual records (i.e. rows). Hence, we should be able to create an additional row based on this split. To do so, Pandas provides a dedicated function since version 0.25, called `explode`. Starting from a small subset example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example = survey_data.loc[7:10, \"species\"]\n", - "example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `split` method on strings, we can split the string using a given character, in this case the word `and`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example.str.split(\"and\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `explode` method will create a row for each element in the list:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_split = example.str.split(\"and\").explode()\n", - "example_split" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hence, the `DM` and `SH` are now enlisted in separate rows. Other rows remain unchanged. The only remaining issue is the spaces around the characters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_split.iloc[1], example_split.iloc[2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which we can solve again using the string method `strip`, removing the spaces before and after the characters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_split.str.strip()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make this reusable, let's create a dedicated function to combine these steps, called `solve_double_field_entry`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def solve_double_field_entry(df, keyword=\"and\", column=\"verbatimEventDate\"):\n", - " \"\"\"Split on keyword in column for an enumeration and create extra record\n", - "\n", - " Parameters\n", - " ----------\n", - " df: pd.DataFrame\n", - " DataFrame with a double field entry in one or more values\n", - " keyword: str\n", - " word/character to split the double records on\n", - " column: str\n", - " column name to use for the decoupling of the records\n", - " \"\"\"\n", - " df = df.copy() # copy the input DataFrame to avoid editing the original\n", - " df[column] = df[column].str.split(keyword)\n", - " df = df.explode(column)\n", - " df[column] = df[column].str.strip() # remove white space around the words\n", - " return df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function takes a `DataFrame` as input, splits the record into separate rows and returns an updated `DataFrame`. We can use this function to get an update of the `DataFrame`, with an additional row (observation) added by decoupling the specific field. Let's apply this new function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 6**\n", - "\n", - "- Use the function `solve_double_field_entry` to update the `survey_data` by decoupling the double entries. Save the result as a variable `survey_data_decoupled`.\n", - "\n", - "
Hints\n", - "\n", - "- As we added a 'docstring' to the function, we can check our own documentation to know how to use the function and which inputs we should provide. You can use `SHIFT` + `TAB` to explore the documentation just like any other function.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing7.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled[\"species\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled.head(11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create new occurrence identifier" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `record_id` is no longer a unique identifier for each observation after the decoupling of this data set. We will make a new data set specific identifier, by adding a column called `occurrenceID` that takes a new counter as identifier. As a simple and straightforward approach, we will use a new counter for the whole dataset, starting with 1:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.arange(1, len(survey_data_decoupled) + 1, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create a new column with header `occurrenceID` with the values 1 -> 35550 as field values:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled[\"occurrenceID\"] = np.arange(1, len(survey_data_decoupled) + 1, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To overcome the confusion on having both a `record_id` and `occurrenceID` field, we will remove the `record_id` term:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled = survey_data_decoupled.drop(columns=\"record_id\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hence, columns can be `drop`-ped out of a DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting the date values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the survey data set we received, the `month`, `day`, and `year` columns are containing the information about the date, i.e. `eventDate` in DarwinCore terms. We want this data in a ISO format `YYYY-MM-DD`. A convenient Pandas function is the usage of `to_datetime`, which provides multiple options to interpret dates. One of the options is the automatic interpretation of some 'typical' columns, like `year`, `month` and `day`, when passing a `DataFrame`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pd.to_datetime(survey_data_decoupled[[\"year\", \"month\", \"day\"]]) # uncomment the line and test this statement" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is not working, not all dates can be interpreted... We should get some more information on the reason of the errors. By using the option `coerce`, the problem makers will be labeled as a missing value `NaT`. We can count the number of dates that can not be interpreted:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum(pd.to_datetime(survey_data_decoupled[[\"year\", \"month\", \"day\"]], errors='coerce').isna())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 7**\n", - "\n", - "- Make a selection of `survey_data_decoupled` containing those records that can not correctly be interpreted as date values and save the resulting `DataFrame` as a new variable `trouble_makers`\n", - "\n", - "
Hints\n", - "\n", - "- The result of the `.isna()` method is a `Series` of boolean values, which can be used to make a selection (so called boolean indexing or filtering)\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing8.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Checking some charactersitics of the trouble_makers:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trouble_makers.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trouble_makers[\"day\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trouble_makers[\"month\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trouble_makers[\"year\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The issue is the presence of day `31` during the months April and September of the year 2000. At this moment, we would have to recheck the original data in order to know how the issue could be solved. Apparently, - for this specific case - there has been a data-entry problem in 2000, making the `31` days during this period should actually be `30`. It would be optimal to correct this in the source data set, but for the exercise, we will correct it here." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 8**\n", - "\n", - "- Assign in the `DataFrame` `survey_data_decoupled` all of the troublemakers `day` values the value 30 instead of 31.\n", - "\n", - "
Hints\n", - "\n", - "- No `for`-loop is required, but use the same boolean mask to assign the new value to the correct rows.\n", - "- Check `pandas_03b_indexing.ipynb` for the usage of `loc` and `iloc` to assign new values.\n", - "- With `loc`, specify both the selecting for the rows and for the columns (`df.loc[row_indexer, column_indexer] = ..`).\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing9.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we do the parsing again to create a proper `eventDate` field, containing the dates:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled[\"eventDate\"] = \\\n", - " pd.to_datetime(survey_data_decoupled[[\"year\", \"month\", \"day\"]])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 9**\n", - "\n", - "- Check the number of observations for each year. Create a horizontal bar chart with the number of rows/observations for each year.\n", - "\n", - "
Hints\n", - "\n", - "- To get the total number of observations, both the usage of `value_counts` as using `groupby` + `size` will work. `value_counts` is a convenient function when all you need to do is counting rows.\n", - "- When using `value_counts`, the years in the index will no longer be in ascending order. You can chain methods and include a `sort_index()` method to sort these again.\n", - "\n", - "
\n", - "\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing10.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing11.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Currently, the dates are stored in a python specific date format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled[\"eventDate\"].dtype" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is great, because it allows for many functionalities using the `.dt` accessor:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled.eventDate.dt #add a dot (.) and press TAB to explore the date options it provides" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 10**\n", - "\n", - "- Create a horizontal bar chart with the number of records for each year (cfr. supra), but without using the column `year`, using the `eventDate` column directly.\n", - "\n", - "
Hints\n", - "\n", - "- Check the `groupby` + `size` solution of the previous exercise and use this to start with. Replace the `year` inside the `groupby` method...\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing12.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We actually do not need the `day`, `month`, `year` columns anymore, but feel free to use what suits you best." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 11**\n", - "\n", - "- Create a bar chart with the number of records for each day of the week (`dayofweek`)\n", - "\n", - "
Hints\n", - "\n", - "- Pandas has an accessor for `dayofweek` as well.\n", - "- You can specify the days of the week yourself to improve the plot, or use the Python standard library `calendar.day_name` (import the calendar module first) to get the names.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing13.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When saving the information to a file (e.g. `CSV`-file), this data type will be automatically converted to a string representation. However, we could also decide to explicitly provide the string format the dates are stored (losing the date type functionalities), in order to have full control on the way these dates are formatted:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled[\"eventDate\"] = survey_data_decoupled[\"eventDate\"].dt.strftime('%Y-%m-%d')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled[\"eventDate\"].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the remainder, let's remove the day/year/month columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_decoupled = survey_data_decoupled.drop(columns=[\"day\", \"month\", \"year\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Add species names to dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The column `species` only provides a short identifier in the survey overview. The name information is stored in a separate file `species.csv`. We want our data set to include this information, read in the data and add it to our survey data set:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 12**\n", - "\n", - "- Read in the 'species.csv' file and save the resulting `DataFrame` as variable `species_data`.\n", - "\n", - "
Hints\n", - "\n", - "- Check the delimiter (`sep`) parameter of the `read_csv` function.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing14.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "species_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fix a wrong acronym naming" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When reviewing the metadata, you see that in the data-file the acronym `NE` is used to describe `Neotoma albigula`, whereas in the [metadata description](http://esapubs.org/archive/ecol/E090/118/Portal_rodent_metadata.htm), the acronym `NA` is used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 13**\n", - "\n", - "- Convert the value of 'NE' to 'NA' by using Boolean indexing/Filtering for the `species_id` column.\n", - "\n", - "
Hints\n", - "\n", - "- To assign a new value, use the `loc` operator.\n", - "- With `loc`, specify both the selecting for the rows and for the columns (`df.loc[row_indexer, column_indexer] = ..`).\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing15.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Merging surveys and species" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we now prepared the two series, we can combine the data, using again the `pd.merge` operation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to add the data of the species to the survey data, in order to see the full species names in the combined data table." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 14**\n", - "\n", - "Combine the DataFrames `survey_data_decoupled` and `species_data` by adding the corresponding species information (name, class, kingdom,..) to the individual observations. Assign the output to a new variable `survey_data_species`.\n", - "\n", - "
Hints\n", - "\n", - "- This is an example of a database JOIN operation. Pandas provides the `pd.merge` function to join two data sets using a common identifier.\n", - "- Take into account that our key-column is different for `species_data` and `survey_data_decoupled`, respectively `species` and `species_id`. The `pd.merge()` function has `left_on` and `right_on` keywords to specify the name of the column in the left and right `DataFrame` to merge on.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing16.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(survey_data_species) # check length after join operation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The join is ok, but we are left with some redundant columns and wrong naming:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_species.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We do not need the columns `species_x` and `species_id` column anymore, as we will use the scientific names from now on:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_species = survey_data_species.drop([\"species_x\", \"species_id\"], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The column `species_y` could just be named `species`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_species = survey_data_species.rename(columns={\"species_y\": \"species\"})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_species.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "len(survey_data_species)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Add coordinates from the plot locations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the coordinate data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The individual plots are only identified by a `plot` identification number. In order to provide sufficient information to external users, additional information about the coordinates should be added. The coordinates of the individual plots are saved in another file: `plot_location.xlsx`. We will use this information to further enrich our data set and add the Darwin Core Terms `decimalLongitude` and `decimalLatitude`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 15**\n", - "\n", - "- Read the excel file 'plot_location.xlsx' and store the data as the variable `plot_data`, with 3 columns: plot, xutm, yutm.\n", - "\n", - "
Hints\n", - "\n", - "- Pandas read methods all have a similar name, `read_...`.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing17.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Transforming to other coordinate reference system" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These coordinates are in meters, more specifically in the [UTM 12 N](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system) coordinate system. However, the agreed coordinate representation for Darwin Core is the [World Geodetic System 1984 (WGS84)](http://spatialreference.org/ref/epsg/wgs-84/).\n", - "\n", - "As this is not a GIS course, we will shortcut the discussion about different projection systems, but provide an example on how such a conversion from `UTM12N` to `WGS84` can be performed with the projection toolkit `pyproj` and by relying on the existing EPSG codes (a registry originally setup by the association of oil & gas producers)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we define out two projection systems, using their corresponding EPSG codes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyproj import Transformer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "transformer = Transformer.from_crs(\"EPSG:32612\", \"epsg:4326\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The reprojection can be done by the function `transform` of the projection toolkit, providing the coordinate systems and a set of x, y coordinates. For example, for a single coordinate, this can be applied as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "transformer.transform(681222.131658, 3.535262e+06)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Such a transformation is a function not supported by Pandas itself (it is in https://geopandas.org/). In such an situation, we want to _apply_ a custom function to _each row of the DataFrame_. Instead of writing a `for` loop to do this for each of the coordinates in the list, we can `.apply()` this function with Pandas." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 16**\n", - "\n", - "Apply the pyproj function `transform` to plot_data, using the columns `xutm` and `yutm` and save the resulting output in 2 new columns, called `decimalLongitude` and `decimalLatitude`:\n", - "\n", - "- Create a function `transform_utm_to_wgs` that takes a row of a `DataFrame` and returns a `Series` of two elements with the longitude and latitude.\n", - "- Test this function on the first row of `plot_data`\n", - "- Now `apply` this function on all rows (use the `axis` parameter correct)\n", - "- Assign the result of the previous step to `decimalLongitude` and `decimalLatitude` columns\n", - "\n", - "
Hints\n", - "\n", - "- Convert the output of the transformer to a Series before returning (`pd.Series(....)`)\n", - "- A convenient way to select a single row is using the `.loc[0]` operator.\n", - "- `apply` can be used for both rows (`axis` 1) as columns (`axis` 0).\n", - "- To assign two columns at once, you can use a similar syntax as for selecting multiple columns with a list of column names (`df[['col1', 'col2']]`).\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing18.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing19.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing20.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing21.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above function `transform_utm_to_wgs` you have created is a very specific function that knows the structure of the `DataFrame` you will apply it to (it assumes the 'xutm' and 'yutm' column names). We could also make a more generic function that just takes a X and Y coordinate and returns the `Series` of converted coordinates (`transform_utm_to_wgs2(X, Y)`).\n", - "\n", - "An alternative to apply such a custom function to the `plot_data` `DataFrame` is the usage of the `lambda` construct, which lets you specify a function on one line as an argument:\n", - "\n", - " transformer = Transformer.from_crs(\"EPSG:32612\", \"epsg:4326\")\n", - " plot_data.apply(lambda row : transformer.transform(row['xutm'], row['yutm']), axis=1)\n", - "\n", - "\n", - "
\n", - "\n", - "__WARNING__\n", - "\n", - "Do not abuse the usage of the `apply` method, but always look for an existing Pandas function first as these are - in general - faster!\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Join the coordinate information to the survey data set" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can extend our survey data set with this coordinate information. Making the combination of two data sets based on a common identifier is completely similar to the usage of `JOIN` operations in databases. In Pandas, this functionality is provided by [`pd.merge`](http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-DataFrame-joining-merging).\n", - "\n", - "In practice, we have to add the columns `decimalLongitude`/`decimalLatitude` to the current data set `survey_data_species`, by using the plot identification number as key to join." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 17**\n", - "\n", - "- Extract only the columns to join to our survey dataset: the `plot` identifiers, `decimalLatitude` and `decimalLongitude` into a new variable named `plot_data_selection`\n", - "\n", - "
Hints\n", - "\n", - "- To select multiple columns, use a `list` of column names, e.g. `df[[\"my_col1\", \"my_col2\"]]`\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing22.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 18**\n", - "\n", - "Combine the DataFrame `plot_data_selection` and the DataFrame `survey_data_species` by adding the corresponding coordinate information to the individual observations using the `pd.merge()` function. Assign the output to a new variable `survey_data_plots`.\n", - "\n", - "
Hints\n", - "\n", - "- This is an example of a database JOIN operation. Pandas provides the `pd.merge` function to join two data sets using a common identifier.\n", - "- The key-column is the `plot`.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing23.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_plots.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot locations need to be stored with the variable name `verbatimLocality` indicating the identifier as integer value of the plot:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_plots = survey_data_plots.rename(columns={'plot': 'verbatimLocality'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now save our clean data to a `csv` file, so we can further analyze the data in a following notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_plots.to_csv(\"interim_survey_data_species.csv\", index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## (OPTIONAL SECTION) 4. Using a API service to match the scientific names" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As the current species names are rather short and could eventually lead to confusion when shared with other users, retrieving additional information about the different species in our dataset would be useful to integrate our work with other research. An option is to match our names with an external service to request additional information about the different species.\n", - "\n", - "One of these services is [GBIF API](http://www.gbif.org/developer/species). The service can most easily be illustrated with a small example:

\n", - "In a new tab blad of the browser, go to the URL [http://www.gbif.org/species/2475532](http://www.gbif.org/species/2475532), which corresponds to the page of `Alcedo atthis` (*ijsvogel* in dutch). One could for each of the species in the list we have do a search on the website of GBIF to find the corresponding page of the different species, from which more information can be extracted manually. However, this would take a lot of time...\n", - "\n", - "Therefore, GBIF (as many other organizations!) provides a service (or API) to extract the same information in a machine-readable way, in order to automate these searches. As an example, let's search for the information of `Alcedo atthis`, using the GBIF API: Go to the URL: [http://api.gbif.org/v1/species/match?name=Alcedo atthis](http://api.gbif.org/v1/species/match?name=%22Alcedo%20atthis%22) and check the output. What we did is a machine-based search on the GBIF website for information about `Alcedo atthis`.\n", - "\n", - "The same can be done using Python. The main library we need to this kind of automated searches is the [`requests` package](http://docs.python-requests.org/en/master/), which can be used to do request to any kind of API out there." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example matching with Alcedo Atthis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the example of `Alcedo atthis`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "species_name = 'Alcedo atthis'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "base_string = 'http://api.gbif.org/v1/species/match?'\n", - "request_parameters = {'verbose': False, 'strict': True, 'name': species_name}\n", - "message = requests.get(base_string, params=request_parameters).json()\n", - "message" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From which we get a dictionary containing more information about the taxonomy of the `Alcedo atthis`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the species data set available, the name to match is provided in the combination of two columns, so we have to combine those to in order to execute the name matching:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "genus_name = \"Callipepla\"\n", - "species_name = \"squamata\"\n", - "name_to_match = '{} {}'.format(genus_name, species_name)\n", - "base_string = 'http://api.gbif.org/v1/species/match?'\n", - "request_parameters = {'strict': True, 'name': name_to_match} # use strict matching(!)\n", - "message = requests.get(base_string, params=request_parameters).json()\n", - "message" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To apply this on our species data set, we will have to do this request for each of the individual species/genus combination. As, this is a returning functionality, we will write a small function to do this:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Writing a custom matching function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 19**\n", - "\n", - "- Write a function, called `name_match` that takes the `genus`, the `species` and the option to perform a strict matching or not as inputs, performs a matching with the GBIF name matching API and return the received message as a dictionary.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing24.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**NOTE**\n", - "\n", - "For many of these API request handling, dedicated packages do exist, e.g. pygbif provides different functions to do requests to the GBIF API, basically wrapping the request possibilities. For any kind of service, just ask yourself: is the dedicated library providing sufficient additional advantage, or can I easily setup the request myself. (or sometimes: for which the documentation is the best...)

Many services do exist for a wide range of applications, e.g. scientific name matching, matching of addresses, downloading of data,...\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Testing our custom matching function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "genus_name = \"Callipepla\"\n", - "species_name = \"squamata\"\n", - "name_match(genus_name, species_name, strict=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, the matching won't provide an answer for every search:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "genus_name = \"Lizard\"\n", - "species_name = \"sp.\"\n", - "name_match(genus_name, species_name, strict=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Match each of the species names of the survey data set" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hence, in order to add this information to our survey DataFrame, we need to perform the following steps:\n", - "1. extract the unique genus/species combinations in our dataset and combine them in single column\n", - "2. match each of these names to the GBIF API service\n", - "3. process the returned message:\n", - " * if a match is found, add the information of the columns 'class', 'kingdom', 'order', 'phylum', 'scientificName', 'status' and 'usageKey'\n", - " * if no match was found: nan-values\n", - "4. Join the DataFrame of unique genus/species information with the enriched GBIF info to the `survey_data_plots` data set" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 20**\n", - "\n", - "- Extract the unique combinations of genus and species in the `survey_data_plots` using the function `drop_duplicates()`. Save the result as the variable `unique_species` and remove the `NaN` values using `.dropna()`.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing25.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(unique_species)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 21**\n", - "\n", - "- Extract the unique combinations of genus and species in the `survey_data_plots` using `groupby`. Save the result as the variable `unique_species`.\n", - "\n", - "
Hints\n", - "\n", - "- As `groupby` needs an aggregation function, this can be `first()` (the first of each group) as well.\n", - "- Do not forget to `reset_index` after the `groupby`.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing26.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(unique_species)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 22**\n", - "\n", - "- Combine the columns genus and species to a single column with the complete name, save it in a new column named 'name'\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing27.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "unique_species.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To perform the matching for each of the combination, different options do exist (remember `apply`?)\n", - "\n", - "Just to showcase the possibility of using `for` loops in such a situation, let's do the addition of the matched information with a `for` loop. First, we will store everything in one dictionary, where the keys of the dictionary are the index values of `unique_species` (in order to later merge them again) and the values are the entire messages (which are dictionaries on itself). The format will look as following:\n", - "\n", - "```\n", - "species_annotated = {O: {'canonicalName': 'Squamata', 'class': 'Reptilia', 'classKey': 358, ...},\n", - " 1: {'canonicalName':...},\n", - " 2:...}\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# this will take a bit as we do a request to gbif for each individual species\n", - "species_annotated = {}\n", - "for key, row in unique_species.iterrows():\n", - " species_annotated[key] = name_match(row[\"genus\"], row[\"species\"], strict=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#species_annotated # uncomment to see output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now transform this to a pandas `DataFrame`:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 23**\n", - "\n", - "- Convert the dictionary `species_annotated` into a pandas DataFrame with the row index the key-values corresponding to `unique_species` and the column headers the output columns of the API response. Save the result as the variable `df_species_annotated`.\n", - "\n", - "
Hints\n", - "\n", - "- The documentation of `pd.DataFrame` says the input van be 'ndarray (structured or homogeneous), Iterable, dict, or DataFrame'.\n", - "- `transpose` can be used to flip rows and columns.\n", - "\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing28.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_species_annotated.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Select relevant information and add this to the survey data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 24**\n", - "\n", - "- Subselect the columns 'class', 'kingdom', 'order', 'phylum', 'scientificName', 'status' and 'usageKey' from the DataFrame `df_species_annotated`. Save it as the variable `df_species_annotated_subset`\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing29.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_species_annotated_subset.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 25**\n", - "\n", - "- Join the `df_species_annotated_subset` information to the `unique_species` overview of species. Save the result as variable `unique_species_annotated`.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing30.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "unique_species_annotated.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "**EXERCISE 26**\n", - "\n", - "- Join the `unique_species_annotated` data to the `survey_data_plots` data set, using both the genus and species column as keys. Save the result as the variable `survey_data_completed`.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbtutor-solution" - ] - }, - "outputs": [], - "source": [ - "# %load _solutions/case2_observations_processing31.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(survey_data_completed)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_completed.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Congratulations! You did a great cleaning job, save your result:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data_completed.to_csv(\"survey_data_completed_.csv\", index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Acknowledgements" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* `species.csv` and `survey.csv` are used from the [data carpentry workshop](https://github.com/datacarpentry/python-ecology-lesson) This data is from the paper S. K. Morgan Ernest, Thomas J. Valone, and James H.\n", - "Brown. 2009. Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA. Ecology 90:1708. http://esapubs.org/archive/ecol/E090/118/\n", - "* The `plot_location.xlsx` is a dummy created location file purely created for this exercise, using the plots location on google maps\n", - "* [GBIF API](http://www.gbif.org/developer/summary)" - ] - } - ], - "metadata": { - "celltoolbar": "Nbtutor - export exercises", - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.2" - }, - "nav_menu": {}, - "toc": { - "navigate_menu": true, - "number_sections": true, - "sideBar": true, - "threshold": 6, - "toc_cell": false, - "toc_section_display": "block", - "toc_window_display": true - }, - "toc_position": { - "height": "811px", - "left": "0px", - "right": "1864.67px", - "top": "106px", - "width": "212px" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/case3_bacterial_resistance_lab_experiment.ipynb b/notebooks/case3_bacterial_resistance_lab_experiment.ipynb index a61d8b1..18dab16 100644 --- a/notebooks/case3_bacterial_resistance_lab_experiment.ipynb +++ b/notebooks/case3_bacterial_resistance_lab_experiment.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - Bacterial resistance experiment

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -501,14 +501,13 @@ "- Make sure the `WT(2)` and `MUT(2)` categories are used as respectively `WT` and `MUT` by adjusting them with Pandas first.\n", "- Use the __falcor__ data and the Seaborn package. The 'log10 mutation rate' on the figure corresponds to the `log10 Mc` column.\n", "\n", - "\n", "
Hints\n", "\n", "- To replace values using a mapping (dictionary with the keys the current values and the values the new values), use the Pandas `replace` method.\n", "- This is another example of a `catplot`, using `point`s to represent the data.\n", "- The `join` argument defines if individual points need to be connected or not.\n", - "- One combination appears multiple times, so make sure to not yet use confidence intervals by setting `ci` to `Null`.\n", - "\n", + "- Seaborn provides the `row_order` and `order` options to provide a custom order of respectively the subplots and categories.\n", + "- One combination appears multiple times, so make sure to not yet use confidence intervals by setting `errorbar` to `None`.\n", "
" ] }, @@ -640,7 +639,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/case4_air_quality_analysis.ipynb b/notebooks/case4_air_quality_analysis.ipynb index 04f3520..29f1c4e 100644 --- a/notebooks/case4_air_quality_analysis.ipynb +++ b/notebooks/case4_air_quality_analysis.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - air quality data of European monitoring stations (AirBase)

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -1234,7 +1234,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/notebooks/case4_air_quality_processing.ipynb b/notebooks/case4_air_quality_processing.ipynb index 4cac881..389bd61 100644 --- a/notebooks/case4_air_quality_processing.ipynb +++ b/notebooks/case4_air_quality_processing.ipynb @@ -6,7 +6,7 @@ "source": [ "

CASE - air quality data of European monitoring stations (AirBase)

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -714,7 +714,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/notebooks/pandas_01_data_structures.ipynb b/notebooks/pandas_01_data_structures.ipynb index 9af9f24..89b9442 100644 --- a/notebooks/pandas_01_data_structures.ipynb +++ b/notebooks/pandas_01_data_structures.ipynb @@ -7,7 +7,7 @@ "source": [ "

01 - Pandas: Data Structures

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -765,7 +765,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_02_basic_operations.ipynb b/notebooks/pandas_02_basic_operations.ipynb index 6bd4d96..8231d50 100644 --- a/notebooks/pandas_02_basic_operations.ipynb +++ b/notebooks/pandas_02_basic_operations.ipynb @@ -8,7 +8,7 @@ "

02 - Pandas: Basic operations on Series and DataFrames

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -779,7 +779,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_03a_selecting_data.ipynb b/notebooks/pandas_03a_selecting_data.ipynb index f83092b..99071bc 100644 --- a/notebooks/pandas_03a_selecting_data.ipynb +++ b/notebooks/pandas_03a_selecting_data.ipynb @@ -8,7 +8,7 @@ "

03 - Pandas: Indexing and selecting data - part I

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -655,7 +655,7 @@ "id": "49d05bde", "metadata": {}, "source": [ - "For the quick ones among you, here are some more exercises with some larger dataframe with film data. These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so all credit to him!) and the datasets he prepared for that. You can download these data from here: [`titles.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [`cast.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." + "For the quick ones among you, here are some more exercises with some larger dataframe with film data. These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so all credit to him!) and the datasets he prepared for that. You can download these data from here: [titles.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [cast.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." ] }, { @@ -739,6 +739,20 @@ "# %load _solutions/pandas_03a_selecting_data12.py" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "95a214f4-2ce3-4f56-9dfa-e546acdf12a5", + "metadata": { + "tags": [ + "nbtutor-solution" + ] + }, + "outputs": [], + "source": [ + "# %load _solutions/pandas_03a_selecting_data13.py" + ] + }, { "cell_type": "markdown", "id": "1150e93f", @@ -765,7 +779,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data13.py" + "# %load _solutions/pandas_03a_selecting_data14.py" ] }, { @@ -794,7 +808,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data14.py" + "# %load _solutions/pandas_03a_selecting_data15.py" ] }, { @@ -823,7 +837,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data15.py" + "# %load _solutions/pandas_03a_selecting_data16.py" ] }, { @@ -837,7 +851,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data16.py" + "# %load _solutions/pandas_03a_selecting_data17.py" ] }, { @@ -866,7 +880,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data17.py" + "# %load _solutions/pandas_03a_selecting_data18.py" ] }, { @@ -880,7 +894,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data18.py" + "# %load _solutions/pandas_03a_selecting_data19.py" ] }, { @@ -894,7 +908,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data19.py" + "# %load _solutions/pandas_03a_selecting_data20.py" ] }, { @@ -923,7 +937,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data20.py" + "# %load _solutions/pandas_03a_selecting_data21.py" ] }, { @@ -952,7 +966,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data21.py" + "# %load _solutions/pandas_03a_selecting_data22.py" ] }, { @@ -981,7 +995,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/pandas_03a_selecting_data22.py" + "# %load _solutions/pandas_03a_selecting_data23.py" ] }, { @@ -1017,7 +1031,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_03b_indexing.ipynb b/notebooks/pandas_03b_indexing.ipynb index 0a7cb63..bb51862 100644 --- a/notebooks/pandas_03b_indexing.ipynb +++ b/notebooks/pandas_03b_indexing.ipynb @@ -7,7 +7,7 @@ "source": [ "

03 - Pandas: Indexing and selecting data - part II

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -694,7 +694,7 @@ "formats": "ipynb,md:myst" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -708,7 +708,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_04_time_series_data.ipynb b/notebooks/pandas_04_time_series_data.ipynb index 5b81201..85037aa 100644 --- a/notebooks/pandas_04_time_series_data.ipynb +++ b/notebooks/pandas_04_time_series_data.ipynb @@ -7,7 +7,7 @@ "source": [ "

04 - Pandas: Working with time series data

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -682,11 +682,10 @@ "source": [ "
\n", "\n", - "EXERCISE 1:\n", + "**EXERCISE 1:\n", + "\n", + "* select all data starting from 2012\n", "\n", - "
    \n", - "
  • select all data starting from 2012
    • \n", - "
\n", "
" ] }, @@ -711,11 +710,16 @@ "source": [ "
\n", "\n", - "EXERCISE 2:\n", + "**EXERCISE 2**:\n", + "\n", + "* select all data in January for all different years\n", + "\n", + "
Hints\n", + "\n", + "* Remember you can get information about the month of each timestamp using the `month` attribute of the DatetimeIndex.\n", + "\n", + "
\n", "\n", - "
    \n", - "
  • select all data in January for all different years
    • \n", - "
\n", "
" ] }, @@ -740,11 +744,16 @@ "source": [ "
\n", "\n", - "EXERCISE 3:\n", + "**EXERCISE 3**:\n", + "\n", + "* select all data in April, May and June for all different years\n", + "\n", + "
Hints\n", + "\n", + "* If you want to check for equality with multiple possible values (equal to x OR equal to y OR ...), the `isin()` method can be easier to use.\n", + "\n", + "
\n", "\n", - "
    \n", - "
  • select all data in April, May and June for all different years
    • \n", - "
\n", "
" ] }, @@ -994,14 +1003,6 @@ "source": [ "# %load _solutions/pandas_04_time_series_data9.py" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a643acc-c5e9-458a-ad7f-a782bd45db5c", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1023,7 +1024,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_05_groupby_operations.ipynb b/notebooks/pandas_05_groupby_operations.ipynb index 8caf506..b53af88 100644 --- a/notebooks/pandas_05_groupby_operations.ipynb +++ b/notebooks/pandas_05_groupby_operations.ipynb @@ -5,10 +5,10 @@ "id": "804eea96", "metadata": {}, "source": [ - "

06 - Pandas: \"Group by\" operations

\n", + "

05 - Pandas: \"Group by\" operations

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -372,7 +372,7 @@ "\n", "**EXERCISE 6**:\n", "\n", - "* Make a bar plot to visualize the average Fare payed by people depending on their age. The age column is divided is separate classes using the `pd.cut()` function as provided below.\n", + "* Make a bar plot to visualize the average Fare payed by people depending on their age. The age column is divided in separate classes using the `pd.cut()` function as provided below.\n", "\n", "
" ] @@ -501,7 +501,7 @@ "id": "580c1483", "metadata": {}, "source": [ - "Another way to obtain such counts, is to use the Series `value_counts` method:" + "Another way to obtain such counts, is to use the Series `value_counts` method (which additionally sorts from most to least occurring group):" ] }, { @@ -527,7 +527,7 @@ "id": "7e3afc02", "metadata": {}, "source": [ - "These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so credit to him!) and the datasets he prepared for that. You can download these data from here: [`titles.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [`cast.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." + "These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so credit to him!) and the datasets he prepared for that. You can download these data from here: [titles.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [cast.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." ] }, { @@ -584,11 +584,11 @@ "source": [ "
\n", "\n", - "EXERCISE 7:\n", + "**EXERCISE 7**:\n", + "\n", + "\n", + "* Using `groupby()`, plot the number of films that have been released each decade in the history of cinema.\n", "\n", - "
    \n", - "
  • Using `groupby()`, plot the number of films that have been released each decade in the history of cinema.
    • \n", - "
\n", "
" ] }, @@ -1225,7 +1225,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_06_data_cleaning.ipynb b/notebooks/pandas_06_data_cleaning.ipynb index 2ce0765..d6428b8 100644 --- a/notebooks/pandas_06_data_cleaning.ipynb +++ b/notebooks/pandas_06_data_cleaning.ipynb @@ -5,10 +5,9 @@ "id": "a4812285-6a36-429e-835d-5c5584c9e9e2", "metadata": {}, "source": [ - "

Pandas: Methods for data cleaning

\n", + "

06 - Pandas: Methods for data cleaning

\n", "\n", - "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -920,7 +919,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_07_missing_values.ipynb b/notebooks/pandas_07_missing_values.ipynb index cd056a5..0869c2f 100644 --- a/notebooks/pandas_07_missing_values.ipynb +++ b/notebooks/pandas_07_missing_values.ipynb @@ -5,10 +5,10 @@ "id": "8bd0774d", "metadata": {}, "source": [ - "

08 - Pandas: Working with missing data

\n", + "

07 - Pandas: Working with missing data

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -312,7 +312,7 @@ "\n", "**REMEMBER**:
\n", "\n", - "* missing value indicator: `np.nan` (`NaN`)\n", + "* Missing value indicator: `np.nan` (`NaN`)\n", "* Reductions: skipped by default\n", "* Mathematical operations (eg `+`): propagate by default\n", "* Specific functions:\n", @@ -351,7 +351,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_08_reshaping_data.ipynb b/notebooks/pandas_08_reshaping_data.ipynb index 9548064..33f732a 100644 --- a/notebooks/pandas_08_reshaping_data.ipynb +++ b/notebooks/pandas_08_reshaping_data.ipynb @@ -5,10 +5,10 @@ "id": "a7d814f2", "metadata": {}, "source": [ - "

07 - Pandas: Tidy data and reshaping

\n", + "

08 - Pandas: Tidy data and reshaping

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -121,7 +121,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8aaf53e9-cfba-4d32-989b-9f928f5284a2", + "id": "6d54b4ec-7d96-4906-88df-16b06ac43d21", "metadata": {}, "outputs": [], "source": [ @@ -1029,7 +1029,7 @@ "id": "45ce16c2", "metadata": {}, "source": [ - "These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so credit to him!) and the datasets he prepared for that. You can download these data from here: [`titles.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [`cast.csv`](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." + "These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so credit to him!) and the datasets he prepared for that. You can download these data from here: [titles.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/titles.csv) and [cast.csv](https://course-python-data.s3.eu-central-1.amazonaws.com/cast.csv) and put them in the `/notebooks/data` folder." ] }, { @@ -1232,7 +1232,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/pandas_09_combining_datasets.ipynb b/notebooks/pandas_09_combining_datasets.ipynb index b2dc1cd..308dbc7 100644 --- a/notebooks/pandas_09_combining_datasets.ipynb +++ b/notebooks/pandas_09_combining_datasets.ipynb @@ -5,10 +5,10 @@ "id": "be5c9d31", "metadata": {}, "source": [ - "

Pandas: Combining datasets Part I - concat

\n", + "

09 - Pandas: Combining datasets

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -735,7 +735,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_by_muni = df.groupby(\"CD_REFNIS\").sum()" + "df_by_muni = df.groupby(\"CD_REFNIS\")[['MS_NUM_VAT', 'MS_NUM_VAT_START', 'MS_NUM_VAT_STOP']].sum()" ] }, { @@ -921,14 +921,6 @@ "source": [ "pd.concat([countries2, country_economics], axis=\"columns\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c420d1a6-5cf6-4efa-868d-0c591459e4e1", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -950,7 +942,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/visualization_01_matplotlib.ipynb b/notebooks/visualization_01_matplotlib.ipynb index 814532d..4e72cae 100644 --- a/notebooks/visualization_01_matplotlib.ipynb +++ b/notebooks/visualization_01_matplotlib.ipynb @@ -6,7 +6,7 @@ "source": [ "

Visualization - Matplotlib

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -885,7 +885,7 @@ "- In the left subplot, make a bar chart of the minimal measured value for each of the station.\n", "- In the right subplot, make a bar chart of the maximal measured value for each of the station. \n", "\n", - "Add a title to the Figure containing 'Minimal and maximal discharge from 2009-01-01 till 2013-01-02'. Extract these dates from the data itself instead of hardcoding it.\n", + "Add a title to the Figure containing 'Minimal and maximal discharge from 2009-01-01 till 2013-01-02'. Extract these dates from the data itself instead of hardcoding it by using Python f-strings ([_formatted string literals_](https://docs.python.org/3/tutorial/inputoutput.html#formatted-string-literals)).\n", "\n", "
Hints\n", "\n", @@ -898,6 +898,25 @@ "
" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of the usage of formatting f-strings [_formatted string literals_](https://docs.python.org/3/tutorial/inputoutput.html#formatted-string-literals):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_float_variable = 3.14159\n", + "my_datetime_variable = pd.Timestamp(\"2022-12-06\")\n", + "print(f\"Adjust the rounding of a float, {my_float_variable} vs {my_float_variable:.2f}\")\n", + "print(f\"Adjust the rounding of a timestamp, {my_datetime_variable} vs {my_datetime_variable:%Y-%m-%d}.\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1014,7 +1033,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/notebooks/visualization_02_seaborn.ipynb b/notebooks/visualization_02_seaborn.ipynb index e20606f..ebbef1a 100644 --- a/notebooks/visualization_02_seaborn.ipynb +++ b/notebooks/visualization_02_seaborn.ipynb @@ -7,7 +7,7 @@ "

Visualisation: Seaborn

\n", "\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -489,51 +489,6 @@ "
" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary statistics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Aggregations such as `count`, `mean` are embedded in Seaborn (similar to other 'Grammar of Graphics' packages such as ggplot in R and plotnine/altair in Python). We can do these operations directly on the original `titanic` data set in a single coding step:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "sns.catplot(data=titanic, x=\"Survived\", col=\"Pclass\", \n", - " kind=\"count\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use another statistical function to apply on each of the groups, use the `estimator`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sns.catplot(data=titanic, x=\"Sex\", y=\"Age\", col=\"Pclass\", kind=\"bar\", \n", - " estimator=np.mean)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -632,6 +587,53 @@ "# %load _solutions/visualization_02_seaborn3.py" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summary statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aggregations such as `count`, `mean` are embedded in Seaborn (similar to other 'Grammar of Graphics' packages such as ggplot in R and plotnine/altair in Python). We can do these operations directly on the original `titanic` data set in a single coding step:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "sns.catplot(data=titanic, x=\"Survived\", col=\"Pclass\", \n", + " kind=\"count\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use another statistical function to apply on each of the groups, use the `estimator`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sns.catplot(data=titanic, x=\"Sex\", y=\"Age\", col=\"Pclass\", kind=\"bar\", \n", + " estimator=np.mean)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -971,30 +973,27 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "nbtutor-solution" + ] + }, "outputs": [], "source": [ - "casualties_motorway_trucks = casualties[\n", - " (casualties[\"road_type\"] == \"Motorway\")\n", - " & casualties[\"road_user_type\"].isin([\"Light truck\", \"Truck\"])\n", - "]" + "# %load _solutions/visualization_02_seaborn8.py" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "nbtutor-solution" + ] + }, "outputs": [], "source": [ - "sns.catplot(data=casualties_motorway_trucks,\n", - " x=\"week_day\",\n", - " y=\"n_victims\",\n", - " estimator=np.sum,\n", - " ci=None,\n", - " kind=\"bar\",\n", - " color=\"#900C3F\",\n", - " height=3, \n", - " aspect=4)" + "# %load _solutions/visualization_02_seaborn9.py" ] }, { @@ -1035,7 +1034,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn8.py" + "# %load _solutions/visualization_02_seaborn10.py" ] }, { @@ -1048,7 +1047,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn9.py" + "# %load _solutions/visualization_02_seaborn11.py" ] }, { @@ -1089,7 +1088,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn10.py" + "# %load _solutions/visualization_02_seaborn12.py" ] }, { @@ -1102,7 +1101,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn11.py" + "# %load _solutions/visualization_02_seaborn13.py" ] }, { @@ -1115,7 +1114,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn12.py" + "# %load _solutions/visualization_02_seaborn14.py" ] }, { @@ -1128,7 +1127,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn13.py" + "# %load _solutions/visualization_02_seaborn15.py" ] }, { @@ -1161,7 +1160,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn14.py" + "# %load _solutions/visualization_02_seaborn16.py" ] }, { @@ -1174,7 +1173,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn15.py" + "# %load _solutions/visualization_02_seaborn17.py" ] }, { @@ -1212,7 +1211,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn16.py" + "# %load _solutions/visualization_02_seaborn18.py" ] }, { @@ -1225,7 +1224,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn17.py" + "# %load _solutions/visualization_02_seaborn19.py" ] }, { @@ -1273,7 +1272,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn18.py" + "# %load _solutions/visualization_02_seaborn20.py" ] }, { @@ -1286,7 +1285,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn19.py" + "# %load _solutions/visualization_02_seaborn21.py" ] }, { @@ -1299,7 +1298,7 @@ }, "outputs": [], "source": [ - "# %load _solutions/visualization_02_seaborn20.py" + "# %load _solutions/visualization_02_seaborn22.py" ] }, { @@ -1421,7 +1420,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.4" }, "nav_menu": {}, "toc": { diff --git a/notebooks/visualization_03_landscape.ipynb b/notebooks/visualization_03_landscape.ipynb index 05ac5b1..b55c9f4 100644 --- a/notebooks/visualization_03_landscape.ipynb +++ b/notebooks/visualization_03_landscape.ipynb @@ -6,7 +6,7 @@ "source": [ "

Visualization - Python's Visualization Landscape

\n", "\n", - "> *© 2021, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", + "> *© 2022, Joris Van den Bossche and Stijn Van Hoey (, ). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] @@ -24,15 +24,15 @@ "\n", "```\n", "conda install -c conda-forge bokeh plotly altair hvplot holoviews\n", + "conda update -c conda-forge panel\n", "``` \n", "\n", - "To have support of Bokeh inside the Jupyter Lab environment\n", + "To have support of plotly inside the Jupyter Lab environment\n", "```\n", - "conda install jupyter_bokeh\n", + "conda install \"ipywidgets>=7.6\" \n", + "conda install -c conda-forge -c plotly jupyter-dash \n", "```\n", "\n", - "To have support of plotly inside the Jupyter Lab environment everything should be included as suc. However, when seeing only blank/empty space as output in the Jupyter lab environment. Close the Jupyter lab, run the command `jupyter lab build` from the command line and restart the Jupyter lab environment.\n", - "\n", "To run the large data set section, additional package installations are required:\n", "\n", "```\n", @@ -139,7 +139,7 @@ " g = sns.catplot(data=titanic, \n", " x=\"Pclass\", y=\"Survived\", \n", " kind=\"bar\", estimator=np.mean,\n", - " ci=None, color=\"grey\")\n", + " errorbar=None, color=\"grey\")\n", " \n", " g.set_axis_labels(\"Cabin class\", \"Survival rate\")" ] @@ -700,7 +700,7 @@ "source": [ "import hvplot.pandas\n", "\n", - "flow_data.hvplot()" + "flow_data.plot()" ] }, { @@ -812,8 +812,7 @@ "# plotly express does not provide the count statistics out of the box, so calculating these\n", "survival_rate = titanic.groupby(\"Pclass\")['Survived'].mean().reset_index() \n", "\n", - "fig = px.bar(survival_rate, x=\"Pclass\", y=\"Survived\")\n", - "fig.show()" + "px.bar(survival_rate, x=\"Pclass\", y=\"Survived\")" ] }, { @@ -1188,7 +1187,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { From 2ca9e4f4bf8b28d27acc68e7efa5a885c666fa40 Mon Sep 17 00:00:00 2001 From: Joris Van den Bossche Date: Mon, 12 Dec 2022 15:59:28 +0100 Subject: [PATCH 3/4] Add observations.csv data --- notebooks/data/observations.csv | 35551 ++++++++++++++++++++++++++++++ 1 file changed, 35551 insertions(+) create mode 100644 notebooks/data/observations.csv diff --git a/notebooks/data/observations.csv b/notebooks/data/observations.csv new file mode 100644 index 0000000..212f0e8 --- /dev/null +++ b/notebooks/data/observations.csv @@ -0,0 +1,35551 @@ +verbatimLocality,verbatimSex,weight,occurrenceID,decimalLongitude,decimalLatitude,species_ID,day,month,year +2,M,,1,-109.08197499999642,31.93888700000037,,16,7,1977 +3,M,,2,-109.08120799999641,31.938896000000387,,16,7,1977 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+15,,,35545,-109.08103599999642,31.937059000000463,,31,12,2002 +15,,,35546,-109.08103599999642,31.937059000000463,2437568,31,12,2002 +15,,,35547,-109.08103599999642,31.937059000000463,2437568,31,12,2002 +10,F,14.0,35548,-109.08009099999636,31.938017000000425,2437874,31,12,2002 +7,M,51.0,35549,-109.08281599999641,31.938113000000413,2439541,31,12,2002 +5,,,35550,-109.07960199999636,31.93897000000045,,31,12,2002 From a4e86859f4d42e27269d778c0b3859296715d247 Mon Sep 17 00:00:00 2001 From: Joris Van den Bossche Date: Mon, 12 Dec 2022 16:33:21 +0100 Subject: [PATCH 4/4] add species_names --- notebooks/data/species_names.csv | 39 ++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 notebooks/data/species_names.csv diff --git a/notebooks/data/species_names.csv b/notebooks/data/species_names.csv new file mode 100644 index 0000000..172d6df --- /dev/null +++ b/notebooks/data/species_names.csv @@ -0,0 +1,39 @@ +name,class,kingdom,order,phylum,scientificName,ID,taxa +Dipodomys merriami,Mammalia,Animalia,Rodentia,Chordata,"Dipodomys merriami Mearns, 1890",2439521,Rodent +Perognathus flavus,Mammalia,Animalia,Rodentia,Chordata,"Perognathus flavus Baird, 1855",2439566,Rodent +Peromyscus eremicus,Mammalia,Animalia,Rodentia,Chordata,"Peromyscus eremicus (Baird, 1857)",2437981,Rodent +Sigmodon hispidus,Mammalia,Animalia,Rodentia,Chordata,"Sigmodon hispidus Say & Ord, 1825",2438147,Rodent +Dipodomys spectabilis,Mammalia,Animalia,Rodentia,Chordata,"Dipodomys spectabilis Merriam, 1890",2439531,Rodent +Chaetodipus penicillatus,Mammalia,Animalia,Rodentia,Chordata,"Chaetodipus penicillatus (Woodhouse, 1852)",2439591,Rodent +Onychomys torridus,Mammalia,Animalia,Rodentia,Chordata,"Onychomys torridus (Coues, 1874)",2438516,Rodent +Dipodomys ordii,Mammalia,Animalia,Rodentia,Chordata,"Dipodomys ordii Woodhouse, 1853",2439541,Rodent +Spermophilus spilosoma,Mammalia,Animalia,Rodentia,Chordata,"Spermophilus spilosoma Bennett, 1833",2437300,Rodent-not censused +Onychomys leucogaster,Mammalia,Animalia,Rodentia,Chordata,"Onychomys leucogaster (Wied-Neuwied, 1841)",2438517,Rodent +Reithrodontomys megalotis,Mammalia,Animalia,Rodentia,Chordata,"Reithrodontomys megalotis (Baird, 1857)",2437874,Rodent +Sylvilagus audubonii,Mammalia,Animalia,Lagomorpha,Chordata,"Sylvilagus audubonii (Baird, 1858)",2436910,Rabbit +Peromyscus maniculatus,Mammalia,Animalia,Rodentia,Chordata,"Peromyscus maniculatus (Wagner, 1845)",2437967,Rodent +Ammospermophilus harrisi,Mammalia,Animalia,Rodentia,Chordata,"Ammospermophilus harrisii (Audubon & Bachman, 1854)",2437568,Rodent-not censused +Amphispiza bilineata,Aves,Animalia,Passeriformes,Chordata,"Amphispiza bilineata (Cassin, 1850)",2491757,Bird +Campylorhynchus brunneicapillus,Aves,Animalia,Passeriformes,Chordata,"Campylorhynchus brunneicapillus (Lafresnaye, 1835)",5231474,Bird +Calamospiza melanocorys,Aves,Animalia,Passeriformes,Chordata,"Calamospiza melanocorys Stejneger, 1885",2491893,Bird +Callipepla squamata,Aves,Animalia,Galliformes,Chordata,"Callipepla squamata (Vigors, 1830)",5228075,Bird +Reithrodontomys fulvescens,Mammalia,Animalia,Rodentia,Chordata,"Reithrodontomys fulvescens J.A.Allen, 1894",2437864,Rodent +Pipilo chlorurus,Aves,Animalia,Passeriformes,Chordata,"Pipilo chlorurus (Audubon, 1839)",2491276,Bird +Pooecetes gramineus,Aves,Animalia,Passeriformes,Chordata,"Pooecetes gramineus (J.F.Gmelin, 1789)",2491728,Bird +Perognathus hispidus,Mammalia,Animalia,Rodentia,Chordata,"Perognathus hispidus Baird, 1858",2439584,Rodent +Pipilo fuscus,Aves,Animalia,Passeriformes,Chordata,"Pipilo fuscus Swainson, 1827",2491244,Bird +Crotalus viridis,Reptilia,Animalia,Squamata,Chordata,"Crotalus viridis Rafinesque, 1818",8945077,Reptile +Zonotrichia leucophrys,Aves,Animalia,Passeriformes,Chordata,"Zonotrichia leucophrys (J.R.Forster, 1772)",5231132,Bird +Sceloporus clarki,Reptilia,Animalia,Squamata,Chordata,"Sceloporus clarkii Baird & Girard, 1852",2451192,Reptile +Baiomys taylori,Mammalia,Animalia,Rodentia,Chordata,"Baiomys taylori (Thomas, 1887)",2438866,Rodent +Sigmodon fulviventer,Mammalia,Animalia,Rodentia,Chordata,"Sigmodon fulviventer J.A.Allen, 1889",2438153,Rodent +Reithrodontomys montanus,Mammalia,Animalia,Rodentia,Chordata,"Reithrodontomys montanus (Baird, 1855)",2437866,Rodent +Ammodramus savannarum,Aves,Animalia,Passeriformes,Chordata,"Ammodramus savannarum (J.F.Gmelin, 1789)",2491123,Bird +Sigmodon ochrognathus,Mammalia,Animalia,Rodentia,Chordata,"Sigmodon ochrognathus Bailey, 1902",2438156,Rodent +Chaetodipus intermedius,Mammalia,Animalia,Rodentia,Chordata,"Chaetodipus intermedius (Merriam, 1889)",2439589,Rodent +Spermophilus tereticaudus,Mammalia,Animalia,Rodentia,Chordata,"Spermophilus tereticaudus Baird, 1858",2437325,Rodent-not censused +Cnemidophorus uniparens,Reptilia,Animalia,Squamata,Chordata,"Cnemidophorus uniparens Wright & Lowe, 1965",5227544,Reptile +Sceloporus undulatus,Reptilia,Animalia,Squamata,Chordata,"Sceloporus undulatus (Bosc & Daudin, 1801)",2451347,Reptile +Chaetodipus baileyi,Mammalia,Animalia,Rodentia,Chordata,"Chaetodipus baileyi (Merriam, 1894)",2439581,Rodent +Peromyscus leucopus,Mammalia,Animalia,Rodentia,Chordata,"Peromyscus leucopus (Rafinesque, 1818)",2438019,Rodent +Cnemidophorus tigris,Reptilia,Animalia,Squamata,Chordata,"Cnemidophorus tigris Grismer, 1999",8071886,Reptile