Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

exercicio semana 11 #2

Open
wants to merge 2 commits into
base: main
Choose a base branch
from
Open

exercicio semana 11 #2

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
dba36a1
exercicios aula
andrezataina Aug 10, 2024
6ac67ba
exercicio de casa
andrezataina Aug 12, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
60 changes: 60 additions & 0 deletions exercicios/para-casa/exercicio-andreza-silva.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,60 @@
import pandas as pd

'''['Track', 'Album Name', 'Artist', 'Release Date', 'ISRC',
'All Time Rank', 'Track Score', 'Spotify Streams',
'Spotify Playlist Count', 'Spotify Playlist Reach',
'Spotify Popularity', 'YouTube Views', 'YouTube Likes', 'TikTok Posts',
'TikTok Likes', 'TikTok Views', 'YouTube Playlist Reach',
'Apple Music Playlist Count', 'AirPlay Spins', 'SiriusXM Spins',
'Deezer Playlist Count', 'Deezer Playlist Reach',
'Amazon Playlist Count', 'Pandora Streams', 'Pandora Track Stations',
'Soundcloud Streams', 'Shazam Counts', 'TIDAL Popularity',
'Explicit Track'],'''


df = pd.read_csv("C:/Users/andre/OneDrive/Área de Trabalho/REPROGRAMA/SEMANA 11/on33-python-s09-pandas-numpy-I/material/mais_ouvidas_2024.csv")

#print(df)

#print(df.head())
#print(df.columns)

#print(df.dtypes)

df['All Time Rank'] = df['All Time Rank'].replace({',': ''}, regex=True).astype(int) #Converte a coluna 'Spotify Streams' para tipo numérico, removendo separadores de milhar e convertendo com astype
#print(df)
df['Spotify Streams'] = df['Spotify Streams'].replace({',': ''}, regex=True).astype(float)
df['Spotify Playlist Count'] = df['Spotify Playlist Count'].replace({',': ''}, regex=True).astype(float)
df['Spotify Playlist Reach'] = df['Spotify Playlist Reach'].replace({',': ''}, regex=True).astype(float)
df['YouTube Views'] = df['YouTube Views'].replace({',': ''}, regex=True).astype(float)
df['YouTube Likes'] = df['YouTube Likes'].replace({',': ''}, regex=True).astype(float)
df['TikTok Posts'] = df['TikTok Posts'].replace({',': ''}, regex=True).astype(float)
df['TikTok Likes'] = df['TikTok Likes'].replace({',': ''}, regex=True).astype(float)
df['TikTok Views'] = df['TikTok Views'].replace({',': ''}, regex=True).astype(float)
df['YouTube Playlist Reach'] = df['YouTube Playlist Reach'].replace({',': ''}, regex=True).astype(float)
df['AirPlay Spins'] = df['AirPlay Spins'].replace({',': ''}, regex=True).astype(float)
df['SiriusXM Spins'] = df['SiriusXM Spins'].replace({',': ''}, regex=True).astype(float)
df['Deezer Playlist Reach'] = df['Deezer Playlist Reach'].replace({',': ''}, regex=True).astype(float)
df['Pandora Streams'] = df['Pandora Streams'].replace({',': ''}, regex=True).astype(float)
df['Pandora Track Stations'] = df['Pandora Track Stations'].replace({',': ''}, regex=True).astype(float)
df['Soundcloud Streams'] = df['Soundcloud Streams'].replace({',': ''}, regex=True).astype(float)
df['Shazam Counts'] = df['Shazam Counts'].replace({',': ''}, regex=True).astype(float)
df['Release Date'] = pd.to_datetime(df['Release Date'], format="mixed")
print(df.dtypes)



df["Streaming Popularity"] = (df["Spotify Popularity"] + df["YouTube Views"] + df["TikTok Likes"] + df["Shazam Counts"]) / 4
Copy link
Collaborator

@manuellysuzik manuellysuzik Aug 15, 2024 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

o pandas tem uma função que faz isso automaticamente, pode tentar assim

Suggested change
df["Streaming Popularity"] = (df["Spotify Popularity"] + df["YouTube Views"] + df["TikTok Likes"] + df["Shazam Counts"]) / 4
df["Streaming Popularity"] = df[["Spotify Popularity"] + df["YouTube Views"] + df["TikTok Likes"] + df["Shazam Counts"]].mean(axis=1)


print(df["Streaming Popularity"])

df["Total Streams"] = df["Spotify Streams"] + df["YouTube Views"] + df["TikTok Views"] + df["Pandora Streams"] + df["Soundcloud Streams"]
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

pode tentar o uso do .sum()

Copy link
Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

obrigada


print(df["Total Streams"])

print(df)

filtered_df = df [(df["Spotify Popularity"] > 80) & (df["Total Streams"] > 1000000)]
print(filtered_df)

filtered_df.to_json("./filtered_list.json", index=False)
49 changes: 49 additions & 0 deletions exercicios/para-sala/ETL_pandas.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,49 @@
import pandas as pd # import o panda como um pd(apelido para o panda)

# A prof pediu para colocar os elementos que sairam pelo df.columns (o nome das colunas)
#'TransactionID', 'Date', 'MobileModel', 'Brand', 'Price', 'UnitsSold','TotalRevenue', 'CustomerAge', 'CustomerGender', 'Location','PaymentMethod'


df = pd.read_csv("C:/Users/andre/OneDrive/Área de Trabalho/REPROGRAMA/SEMANA 11/on33-python-s09-pandas-numpy-I/material/mobile_sales.csv")

# print(df.head()) # o head retorna o df = seria tipo um banco de dados, o head mostra as primeiras 5 linhas

print(df.head(n=10)) # dessa forma vai me mostrar as 10 primeiras linhas, logo n retorna as linhas que solicitei (se n for maior que o número de linhas existentes ele mostra só as linhas existentes, não dá erro)

print(df.columns) # mostra quantas colunas tem no csv e mostra as colunas = o index(os nomes)

# df_valores_nulos =df.isnull() # retorna False se os valores não forem nulos e True se tiver valor nulo na tabela (nulo é espaço vazio)
# print(df_valores_nulos.sum())
print(df.duplicated().sum()) #retorna as duplicadas, usando o Sum mostra o total de duplicadas

print(df.dtypes) # retorna o tipo de dados de cada coluna / object = string

# item = df['Date']
# print(item) # ou print(df['Date']) imprime a coluna Date

#Para converter dados
# o pd.to_datetime - transformamos a coluna date em datetime
df['Date'] = pd.to_datetime(df['Date'], format="mixed") # o mixed mantem o formato e serve para mostrar o formato real que ela tem

print(df.dtypes)

df["Total Sales Value"] = df["Price"] * df["UnitsSold"] # Cria uma nova coluna com o título Total Sales Value através do produto de Price x UnitsSold
print(df["Total Sales Value"]) # print a nova coluna

print(df.columns)

df["Profit Margin"] = (df["Price"] * 0.30) * df["UnitsSold"]
print(df["Profit Margin"]) # print a nova coluna

print(df.columns)

df["Profit Margin 2"] = df["Total Sales Value"] * 0.30
print(df["Profit Margin 2"]) # print a nova coluna

filtered_df = df [(df["Total Sales Value"] > 100_000) & (df["Profit Margin 2"] > 20_000)]
print(filtered_df)

filtered_df2 = df [(df["PaymentMethod"] == "Online")]
print(filtered_df2)

filtered_df.to_csv("./filtered_list.csv", index=False) # Transforma o arquivo filtrado em csv e index = False não coloca os index
63 changes: 63 additions & 0 deletions exercicios/para-sala/filtered_list.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,63 @@
TransactionID,Date,MobileModel,Brand,Price,UnitsSold,TotalRevenue,CustomerAge,CustomerGender,Location,PaymentMethod,Total Sales Value,Profit Margin,Profit Margin 2
79397f68-61ed-4ea8-bcb2-f918d4e6c05b,2024-01-06,direction,Green Inc,1196.95,85,28002.8,32,Female,Port Erik,Online,101740.75,30522.225,30522.225
f7e98db9-cb87-453e-8179-e48ba5443932,2024-03-07,idea,"Massey, Nicholson and Young",1498.13,70,9703.89,45,Female,Port Daryl,Debit Card,104869.1,31460.730000000003,31460.73
e59a8eb1-8448-4719-8502-2c97407d0ff9,2024-01-08,free,Nelson and Sons,1333.31,79,49676.78,45,Female,East Brianstad,Online,105331.48999999999,31599.447,31599.446999999996
b5119fd6-e0d7-44ee-8f87-44f91d42de3f,2024-05-23,law,Roach-Strong,1236.37,89,26408.0,43,Female,Victorview,Credit Card,110036.93,33011.079,33011.079
03d675d2-f4c7-4860-b159-f7df7142b87e,2024-07-07,special,Weaver Ltd,1418.24,99,1877.76,42,Other,Port Ericstad,Credit Card,140405.76,42121.727999999996,42121.728
9b4f4a39-8512-411a-8533-2b1d99cf4e64,2024-06-24,travel,"James, Garcia and Brown",1141.24,94,98242.2,28,Female,Bellview,Cash,107276.56,32182.968,32182.967999999997
8d757a3c-6ffc-4b44-97aa-de01f6bc3b56,2024-06-06,bar,Jordan-Williams,1409.49,81,29360.24,40,Other,Alberttown,Credit Card,114168.69,34250.606999999996,34250.606999999996
8201d6e3-4f18-4911-9b28-c50627fd1640,2024-02-11,test,Walker-White,1444.64,89,12646.8,51,Female,East Kenneth,Cash,128572.96,38571.888,38571.888
8499624a-1c49-4645-9c65-df13c57c87d9,2024-07-01,matter,Andrews LLC,1269.71,96,72588.74,41,Female,Powellmouth,Credit Card,121892.16,36567.648,36567.648
7f36c3a2-ff43-483b-adb8-668e37d16534,2024-07-15,partner,Hebert Inc,1352.48,95,20333.88,32,Other,Crawfordville,Online,128485.6,38545.68,38545.68
56ad37f3-bb16-4f08-90ba-140b27eebd4a,2024-07-01,century,"Bates, Pearson and Hardy",1245.2,81,59695.92,34,Female,Thomasfort,Cash,100861.2,30258.36,30258.359999999997
5a5ebad6-dab7-4388-9961-411136b68e27,2024-01-24,eight,Martin-Carson,1256.1,90,25697.72,26,Other,South Benjamin,Cash,113048.99999999999,33914.7,33914.7
49ee7bcb-01a3-4c71-9273-49d4afd465a4,2024-03-01,son,Anderson-White,1285.81,82,27464.7,44,Female,Port Williamshire,Online,105436.42,31630.926,31630.926
7ebd3c9c-21a9-48d4-802b-50b2ae3d74e6,2024-07-25,play,Cabrera-White,1358.1,83,30654.0,40,Other,New Christina,Credit Card,112722.29999999999,33816.689999999995,33816.689999999995
96845a93-75b3-4a60-b213-201181842f96,2024-04-16,skill,"White, Ford and Andrews",1360.71,84,65262.78,29,Female,Snowfurt,Online,114299.64,34289.892,34289.892
5633dd9e-0ad3-455e-8b00-7236c2379e1b,2024-07-25,security,"Ware, May and Lopez",1485.6,68,51970.4,47,Female,Michaelland,Credit Card,101020.79999999999,30306.239999999998,30306.239999999994
fdf59c83-bd14-459c-a73d-1c17b86a5a94,2024-01-13,effect,"Martin, Smith and Patterson",1317.86,84,8388.09,54,Female,Davisbury,Credit Card,110700.23999999999,33210.07199999999,33210.07199999999
07602482-1535-4857-b739-94bbfbbb2ef8,2024-04-05,property,Torres Inc,1285.42,91,16868.06,58,Other,Stacyborough,Credit Card,116973.22,35091.966,35091.966
74df89fb-b693-457d-a68e-e017937d9fe0,2024-03-20,middle,"Cooper, Mcclain and Cook",1276.97,85,119864.64,50,Female,West Alice,Debit Card,108542.45,32562.735,32562.734999999997
3b05d54f-0bdf-444c-a021-19e842585d6b,2024-05-28,involve,Figueroa LLC,1184.54,89,23249.54,32,Female,South Melissa,Online,105424.06,31627.217999999997,31627.217999999997
b09fb488-824a-416d-8800-bbd06fd3530e,2024-02-07,nothing,"Miller, Hill and Lawson",1278.82,91,61818.67,49,Female,Thomasview,Credit Card,116372.62,34911.78599999999,34911.786
da4115cc-24fb-4ca6-835c-6eeaf8d03cf2,2024-05-27,mention,"Skinner, Ramirez and Kelley",1486.29,68,39261.45,63,Male,Herreraborough,Cash,101067.72,30320.316,30320.316
97b019b5-ee7f-47bb-a9fe-8a23c0ca4b55,2024-05-11,woman,Williamson-Clay,1378.49,80,10696.96,22,Female,Christopherbury,Debit Card,110279.2,33083.759999999995,33083.759999999995
0cc43991-586c-4734-94cc-ff0163883c1f,2024-04-15,situation,Stein-Bridges,1426.72,84,94828.04,41,Male,Espinozamouth,Online,119844.48,35953.344000000005,35953.344
821fa086-7197-4c69-9733-1dcc939af665,2024-04-06,sing,Cobb LLC,1178.88,89,33021.82,53,Male,Jimchester,Online,104920.32,31476.096000000005,31476.096
c07fff34-92b2-42e0-982b-6dd160b071e1,2024-04-02,operation,"Duncan, Mendoza and Mcdowell",1477.14,90,66427.94,51,Other,South Brandon,Debit Card,132942.6,39882.78,39882.78
57a14899-fdc3-4bc4-82fb-dee041cc5085,2024-03-29,build,Andrews-Martin,1134.19,95,55740.27,21,Male,East Brian,Credit Card,107748.05,32324.415,32324.415
1bcdf294-8f88-4cdd-aa1b-5296be5466b4,2024-03-30,expect,"Jackson, White and Brown",1409.21,76,8286.95,43,Other,Jordanfurt,Credit Card,107099.96,32129.987999999998,32129.988
918c1785-2c85-4757-a15e-cfddb38dd38e,2024-05-27,bad,"Mcmahon, Jones and Baker",1317.59,86,102433.02,28,Other,New Charles,Credit Card,113312.73999999999,33993.822,33993.82199999999
0835e90c-955f-47dc-87df-98c314e501a7,2024-06-25,former,Weaver-Thompson,1196.33,87,115585.69,35,Other,Brandonton,Online,104080.70999999999,31224.212999999996,31224.212999999996
d0da1b38-58fd-4b3f-85fa-d9a8f45ee603,2024-01-20,practice,Wilcox PLC,1293.55,95,85958.04,44,Male,Mclaughlinburgh,Cash,122887.25,36866.175,36866.174999999996
83f22533-6d79-48b7-9d4c-e78aadf0595a,2024-01-23,protect,"Burns, Davila and Camacho",1297.7,85,68618.72,36,Female,North Johnport,Debit Card,110304.5,33091.35,33091.35
1c06d99c-d59c-47e8-83ba-38a40a00be41,2024-04-01,artist,Smith-Tucker,1294.76,99,9072.42,42,Male,Greeneview,Debit Card,128181.24,38454.372,38454.372
56f4a3f9-ee2d-4c28-b836-8f183b0333b0,2024-04-05,they,"Kirby, Oneill and Carter",1345.42,94,31312.96,25,Female,Steelemouth,Online,126469.48000000001,37940.844000000005,37940.844000000005
784b0c63-1eb4-42bf-a8e1-de0d1f9bbbb2,2024-05-18,blood,Fleming Group,1465.14,80,11440.5,60,Male,Newmantown,Cash,117211.20000000001,35163.36,35163.36
1cafe067-7e81-46ff-9990-579adaebc2cf,2024-07-08,senior,Jensen-Lowe,1483.91,94,12619.62,29,Female,West Susan,Online,139487.54,41846.262,41846.262
077487a5-61c4-4f29-bd88-49901d7b47e7,2024-04-30,painting,Harris-Bell,1385.88,83,9700.6,41,Other,North Samuel,Online,115028.04000000001,34508.412000000004,34508.412000000004
4fe52be3-0c3e-4098-bc58-8f391cc3fb26,2024-04-29,born,Cunningham-Hawkins,1390.89,79,60684.54,28,Female,Port Fernandomouth,Credit Card,109880.31000000001,32964.093,32964.093
0740c846-3424-4692-afee-088248bbfd37,2024-07-22,figure,Flowers-Erickson,1408.39,97,44118.9,30,Other,South Holly,Online,136613.83000000002,40984.149,40984.149000000005
c1dd718f-8c25-47a9-bc37-5dd6c767199e,2024-02-26,rule,"Vasquez, Roberts and Johnson",1458.6,85,6503.25,19,Other,Bowentown,Credit Card,123980.99999999999,37194.299999999996,37194.299999999996
b563bdbe-055d-41a8-8cea-06be0db9c82c,2024-05-02,possible,"Johnson, Mcconnell and May",1385.71,75,24011.36,57,Male,Sullivanmouth,Credit Card,103928.25,31178.475000000002,31178.475
15b09d84-166a-4a3d-a92b-d6cddc7e46cf,2024-05-15,experience,Young Inc,1341.15,93,99642.96,55,Male,Lawsonbury,Credit Card,124726.95000000001,37418.085,37418.085
6c234dd7-845d-49d8-a506-0d0525939c52,2024-05-14,most,"Weaver, Young and King",1235.36,98,22969.48,25,Female,Micheleshire,Online,121065.27999999998,36319.583999999995,36319.583999999995
173d6e2c-d2d4-4a78-9b56-8ff05194c0be,2024-07-20,thus,Anderson-Burns,1253.69,94,65396.79,23,Male,Brownburgh,Online,117846.86,35354.058000000005,35354.058
26760d0b-ece5-48d9-906f-6511c119a434,2024-04-08,fine,Sampson-Kennedy,1179.65,89,50670.65,39,Other,South Christina,Credit Card,104988.85,31496.655000000002,31496.655
9230af26-83a1-4066-8d7a-8f32cb65a58c,2024-06-12,industry,"Barrett, Figueroa and White",1384.31,86,64326.15,45,Other,North Jeffrey,Debit Card,119050.65999999999,35715.198,35715.198
95bee8ce-4701-4a6b-8a17-a68f2e661677,2024-03-10,director,Dennis-Sanchez,1343.65,96,3301.35,19,Female,Lake Christopher,Online,128990.40000000001,38697.12,38697.12
05dc416c-5c87-4726-88bb-c3f02350f9d4,2024-06-18,easy,Jones-Nguyen,1354.53,88,16047.56,63,Other,West Kayla,Credit Card,119198.64,35759.592,35759.592
06cbf9e5-391e-4727-a5b0-24c93b3f88df,2024-07-03,option,"Hanson, Barron and Castillo",1110.66,93,65983.14,25,Female,Dunnland,Debit Card,103291.38,30987.414000000004,30987.414
385776a8-dd02-47b3-ac81-848385c53e01,2024-01-25,face,"Hester, Lee and Kirby",1309.52,80,52684.5,55,Female,Kellyton,Cash,104761.6,31428.48,31428.48
2ad868ea-e6ec-4c08-90df-28627a36cd19,2024-05-27,science,"Daniels, Rojas and Pearson",1137.5,96,14628.86,29,Male,Sheilaburgh,Online,109200.0,32760.0,32760.0
21cdcebc-fa3e-413a-9702-8fbd7b1d8682,2024-05-20,plant,Thomas Ltd,1217.74,96,65193.3,45,Other,Mooreburgh,Cash,116903.04000000001,35070.912,35070.912000000004
976cc526-100f-41ea-a8fa-6beb56f959f9,2024-02-13,decision,Miller-Jordan,1251.55,95,31085.22,55,Female,Michaelhaven,Debit Card,118897.25,35669.174999999996,35669.174999999996
c4c114b1-252e-4e40-9c57-c08f2d7388bc,2024-02-01,particularly,"Myers, Wilcox and Beck",1466.37,86,28288.0,22,Male,Danielbury,Online,126107.81999999999,37832.346,37832.346
c390e049-3c4f-4b58-ad23-f52c64d7768f,2024-01-18,resource,"Fox, Stevens and Bell",1114.73,91,7388.25,64,Male,East Robertahaven,Debit Card,101440.43000000001,30432.128999999997,30432.129
903f5961-35c7-47b9-a1f1-5492aa15b049,2024-04-06,four,Robinson-Thompson,1223.95,95,8217.44,43,Female,East Adam,Credit Card,116275.25,34882.575,34882.575
cf0ec4eb-6751-4904-9980-9cbacd679c14,2024-03-15,hope,Hamilton-Garcia,1348.6,79,44273.85,55,Female,Tannerfort,Debit Card,106539.4,31961.82,31961.819999999996
f503c272-a176-4704-9011-e47781852269,2024-03-19,huge,Allen-Mays,1349.26,83,98113.14,52,Male,Danielport,Online,111988.58,33596.574,33596.574
246f33f9-10a0-4d0d-82f5-e7c2164caf37,2024-07-14,around,"Carroll, Brown and Bates",1486.13,75,2336.76,20,Female,Amybury,Online,111459.75000000001,33437.925,33437.925
b4df370f-821b-43aa-a876-841b99222c0b,2024-07-01,discussion,"Santiago, Yoder and Stevens",1447.46,73,73070.87,59,Male,Andreaview,Online,105664.58,31699.374,31699.374
fcf20873-f45d-4ae1-ba0a-6333c35a01f6,2024-01-23,watch,Morrison-Stanley,1424.36,79,35283.6,63,Other,Gibbston,Credit Card,112524.43999999999,33757.331999999995,33757.331999999995
41f08915-addb-4966-8628-038c479c619a,2024-01-28,challenge,Brooks Ltd,1386.69,76,28865.7,39,Male,Ronaldchester,Credit Card,105388.44,31616.532,31616.532
1 change: 1 addition & 0 deletions filtered_list.json
View file
Open in desktop

Large diffs are not rendered by default.