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runner authored and runner committed Jun 9, 2023
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Showing 1 changed file with 38 additions and 48 deletions.
86 changes: 38 additions & 48 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -141,14 +141,14 @@ Next, we open the relevant files.

```python
# Run this cell without changes
world_cup_file = open("data/world_cup_2018.json", encoding='utf8')
world_cup_file = open("data/world_cup_2018.json", encoding="utf8")
population_file = open("data/country_populations.csv")
```


```python
# __SOLUTION__
world_cup_file = open("data/world_cup_2018.json", encoding='utf8')
world_cup_file = open("data/world_cup_2018.json", encoding="utf8")
population_file = open("data/country_populations.csv")
```

Expand Down Expand Up @@ -186,7 +186,7 @@ Make sure the `assert` passes, ensuring that `world_cup_data` has the correct ty
assert type(world_cup_data) == dict

# Check that the dictionary has 2 keys, 'name' and 'rounds'
assert list(world_cup_data.keys()) == ['name', 'rounds']
assert list(world_cup_data.keys()) == ["name", "rounds"]
```


Expand All @@ -197,7 +197,7 @@ assert list(world_cup_data.keys()) == ['name', 'rounds']
assert type(world_cup_data) == dict

# Check that the dictionary has 2 keys, 'name' and 'rounds'
assert list(world_cup_data.keys()) == ['name', 'rounds']
assert list(world_cup_data.keys()) == ["name", "rounds"]
```

### Population Data
Expand Down Expand Up @@ -237,6 +237,7 @@ assert type(population_data) == list
# (csv.DictReader interface differs slightly by Python version;
# either a dict or an OrderedDict is fine here)
from collections import OrderedDict

assert type(population_data[0]) == dict or type(population_data[0]) == OrderedDict
```

Expand All @@ -251,6 +252,7 @@ assert type(population_data) == list
# (csv.DictReader interface differs slightly by Python version;
# either a dict or an OrderedDict is fine here)
from collections import OrderedDict

assert type(population_data[0]) == dict or type(population_data[0]) == OrderedDict
```

Expand Down Expand Up @@ -475,7 +477,7 @@ for round_ in rounds:
round_matches = None
# Add them to the overall list of matches
None

matches[0]
```

Expand All @@ -490,7 +492,7 @@ for round_ in rounds:
round_matches = round_["matches"]
# Add them to the overall list of matches
matches.extend(round_matches)

matches[0]
```

Expand Down Expand Up @@ -581,7 +583,7 @@ for match in matches:
None
# Add team2 name value to teams_set
None

teams = sorted(list(teams_set))
print(teams)
```
Expand All @@ -596,7 +598,7 @@ for match in matches:
teams_set.add(match["team1"]["name"])
# Add team2 value to teams_set
teams_set.add(match["team2"]["name"])

teams = sorted(list(teams_set))
print(teams)
```
Expand Down Expand Up @@ -702,7 +704,7 @@ None
combined_data = {}
for team in teams:
combined_data[team] = {"wins": 0}

# Dictionary comprehension:
# (This is preferable once you're familiar with the
# syntax, but don't worry too much about using this
Expand Down Expand Up @@ -786,6 +788,7 @@ The winner is determined by comparing the values associated with the `'score1'`
```python
# Replace None with appropriate code


def find_winner(match):
"""
Given a dictionary containing information about a match,
Expand All @@ -798,14 +801,15 @@ def find_winner(match):
```python
# __SOLUTION__


def find_winner(match):
"""
Given a dictionary containing information about a match,
return the name of the winner (or None in the case of a tie)
"""
score_1 = match["score1"]
score_2 = match["score2"]

if score_1 > score_2:
return match["team1"]["name"]
elif score_2 > score_1:
Expand Down Expand Up @@ -846,7 +850,7 @@ for match in matches:
if winner:
# Add 1 to the associated count of wins
None

# Visually inspect the output to ensure the wins are
# different for different countries
combined_data
Expand All @@ -864,7 +868,7 @@ for match in matches:
if winner:
# Add 1 to the associated count of wins
combined_data[winner]["wins"] += 1

# Visually inspect the output to ensure the wins are
# different for different countries
combined_data
Expand Down Expand Up @@ -1122,8 +1126,8 @@ population_data_filtered = []
for record in population_data:
# Add record to population_data_filtered if relevant
None
len(population_data_filtered) # 27

len(population_data_filtered) # 27
```


Expand All @@ -1136,7 +1140,7 @@ for record in population_data:
# Add record to population_data_filtered if relevant
if (record["Country Name"] in teams) and (record["Year"] == "2018"):
population_data_filtered.append(record)

len(population_data_filtered)
```

Expand Down Expand Up @@ -1218,12 +1222,13 @@ def normalize_location(country_name):
"Egypt, Arab Rep.": "Egypt",
"Iran, Islamic Rep.": "Iran",
"Korea, Rep.": "South Korea",
"United Kingdom": "England"
"United Kingdom": "England",
}
# The .get method returns the corresponding value from
# the dict if present, otherwise returns country_name
return name_sub_dict.get(country_name, country_name)


# Example where normalized location is different
print(normalize_location("Russian Federation"))
# Example where normalized location is the same
Expand All @@ -1243,12 +1248,13 @@ def normalize_location(country_name):
"Egypt, Arab Rep.": "Egypt",
"Iran, Islamic Rep.": "Iran",
"Korea, Rep.": "South Korea",
"United Kingdom": "England"
"United Kingdom": "England",
}
# The .get method returns the corresponding value from
# the dict if present, otherwise returns country_name
return name_sub_dict.get(country_name, country_name)


# Example where normalized location is different
print(normalize_location("Russian Federation"))
# Example where normalized location is the same
Expand Down Expand Up @@ -1276,8 +1282,8 @@ for record in population_data:
None
# Append to list
None
len(population_data_filtered) # 32

len(population_data_filtered) # 32
```


Expand All @@ -1295,7 +1301,7 @@ for record in population_data:
record["Country Name"] = normalized_name
# Append to list
population_data_filtered.append(record)

len(population_data_filtered)
```

Expand Down Expand Up @@ -1345,7 +1351,7 @@ In the cell below, loop over `population_data_filtered` and convert the data typ
for record in population_data_filtered:
# Convert the population value from str to int
None

# Look at the last record to make sure the population
# value is an int
population_data_filtered[-1]
Expand All @@ -1357,7 +1363,7 @@ population_data_filtered[-1]
for record in population_data_filtered:
# Convert the population value from str to int
record["Value"] = int(record["Value"])

# Look at the last record to make sure the population
# value is an int
population_data_filtered[-1]
Expand Down Expand Up @@ -1463,7 +1469,7 @@ for record in population_data_filtered:
population = None
# Add this information to combined_data
None

# Look combined_data
combined_data
```
Expand All @@ -1478,7 +1484,7 @@ for record in population_data_filtered:
population = record["Value"]
# Add this information to combined_data
combined_data[country]["population"] = population

# Look combined_data
combined_data
```
Expand Down Expand Up @@ -1681,21 +1687,17 @@ A **scatter plot** is he most sensible form of data visualization for showing th
fig, ax = plt.subplots(figsize=(8, 5))

# Basic scatter plot
ax.scatter(
x=populations,
y=wins,
color="gray", alpha=0.5, s=100
)
ax.scatter(x=populations, y=wins, color="gray", alpha=0.5, s=100)
ax.set_xlabel("2018 Population")
ax.set_ylabel("2018 World Cup Wins")
ax.set_title("Population vs. World Cup Wins")

# Add annotations for specific points of interest
highlighted_points = {
"Belgium": 2, # Numbers are the index of that
"Belgium": 2, # Numbers are the index of that
"Brazil": 3, # country in populations & wins
"France": 10,
"Nigeria": 17
"Nigeria": 17,
}
for country, index in highlighted_points.items():
# Get x and y position of data point
Expand All @@ -1706,11 +1708,7 @@ for country, index in highlighted_points.items():
xtext = x - (1.25e6 * len(country))
ytext = y - 0.5
# Annotate with relevant arguments
ax.annotate(
text=country,
xy=(x, y),
xytext=(xtext, ytext)
)
ax.annotate(text=country, xy=(x, y), xytext=(xtext, ytext))
```


Expand All @@ -1721,21 +1719,17 @@ for country, index in highlighted_points.items():
fig, ax = plt.subplots(figsize=(8, 5))

# Basic scatter plot
ax.scatter(
x=populations,
y=wins,
color="gray", alpha=0.5, s=100
)
ax.scatter(x=populations, y=wins, color="gray", alpha=0.5, s=100)
ax.set_xlabel("2018 Population")
ax.set_ylabel("2018 World Cup Wins")
ax.set_title("Population vs. World Cup Wins")

# Add annotations for specific points of interest
highlighted_points = {
"Belgium": 2, # Numbers are the index of that
"Belgium": 2, # Numbers are the index of that
"Brazil": 3, # country in populations & wins
"France": 10,
"Nigeria": 17
"Nigeria": 17,
}
for country, index in highlighted_points.items():
# Get x and y position of data point
Expand All @@ -1746,11 +1740,7 @@ for country, index in highlighted_points.items():
xtext = x - (1.25e6 * len(country))
ytext = y - 0.5
# Annotate with relevant arguments
ax.annotate(
text=country,
xy=(x, y),
xytext=(xtext, ytext)
)
ax.annotate(text=country, xy=(x, y), xytext=(xtext, ytext))
```


Expand Down

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