pandas
http://pythonstudy.xyz/python/article/408-pandas-%EB%8D%B0%EC%9D%B4%ED%83%80-%EB%B6%84%EC%84%9D
start
start
pip install pandas
Pandasは、データ分析に広く使用されているPythonライブラリパッケージです.Name,Salary,Age
John,50000,34
Sally,120000,45
Alyssa,80000,27
csvファイルの準備ができました.import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df["Salary"])
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 50000
1 120000
2 80000
Name: Salary, dtype: int64
出力は.
pandas mutiple columns import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df[['Name','Salary']])
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
Name Salary
0 John 50000
1 Sally 120000
2 Alyssa 80000
pandas columns max , min import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df['Salary'].min())
print(df['Salary'].max())
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
50000
120000
True False import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
ser_of_bool = df['Age'] > 30
print(ser_of_bool)
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 True
1 True
2 False
Name: Age, dtype: bool
ローのみ出力 import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
ser_of_bool = df['Age'] > 30
print(df[ser_of_bool])
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
Name Salary Age
0 John 50000 34
1 Sally 120000 45
ユニーク()リスト出力 import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df['Age'])
print(df['Age'].unique())
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 34
1 45
2 27
Name: Age, dtype: int64
[34 45 27]
https://www.opentutorials.org/module/3873/23171
特定のリストのみ出力 import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = df.values.tolist()
print (products_list)
# [['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
column names
https://datatofish.com/convert-pandas-dataframe-to-list/ import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
print (products_list)
# [['Product', 'Price'], ['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
データフォーマット
import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
f = '{:<8}|{:<15}' # formatting
for i in products_list:
print(f.format(*i))
Product |Price
Tablet |250
iPhone |800
Laptop |1200
Monitor |300
Reference
この問題について(pandas), 我々は、より多くの情報をここで見つけました
https://velog.io/@ash3767/pandas
テキストは自由に共有またはコピーできます。ただし、このドキュメントのURLは参考URLとして残しておいてください。
Collection and Share based on the CC Protocol
Name,Salary,Age
John,50000,34
Sally,120000,45
Alyssa,80000,27
import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df["Salary"])
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 50000
1 120000
2 80000
Name: Salary, dtype: int64
import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df[['Name','Salary']])
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
Name Salary
0 John 50000
1 Sally 120000
2 Alyssa 80000
pandas columns max , min import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df['Salary'].min())
print(df['Salary'].max())
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
50000
120000
True False import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
ser_of_bool = df['Age'] > 30
print(ser_of_bool)
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 True
1 True
2 False
Name: Age, dtype: bool
ローのみ出力 import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
ser_of_bool = df['Age'] > 30
print(df[ser_of_bool])
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
Name Salary Age
0 John 50000 34
1 Sally 120000 45
ユニーク()リスト出力 import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df['Age'])
print(df['Age'].unique())
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 34
1 45
2 27
Name: Age, dtype: int64
[34 45 27]
https://www.opentutorials.org/module/3873/23171
特定のリストのみ出力 import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = df.values.tolist()
print (products_list)
# [['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
column names
https://datatofish.com/convert-pandas-dataframe-to-list/ import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
print (products_list)
# [['Product', 'Price'], ['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
データフォーマット
import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
f = '{:<8}|{:<15}' # formatting
for i in products_list:
print(f.format(*i))
Product |Price
Tablet |250
iPhone |800
Laptop |1200
Monitor |300
Reference
この問題について(pandas), 我々は、より多くの情報をここで見つけました
https://velog.io/@ash3767/pandas
テキストは自由に共有またはコピーできます。ただし、このドキュメントのURLは参考URLとして残しておいてください。
Collection and Share based on the CC Protocol
import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df['Salary'].min())
print(df['Salary'].max())
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
50000
120000
import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
ser_of_bool = df['Age'] > 30
print(ser_of_bool)
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 True
1 True
2 False
Name: Age, dtype: bool
ローのみ出力 import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
ser_of_bool = df['Age'] > 30
print(df[ser_of_bool])
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
Name Salary Age
0 John 50000 34
1 Sally 120000 45
ユニーク()リスト出力 import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df['Age'])
print(df['Age'].unique())
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 34
1 45
2 27
Name: Age, dtype: int64
[34 45 27]
https://www.opentutorials.org/module/3873/23171
特定のリストのみ出力 import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = df.values.tolist()
print (products_list)
# [['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
column names
https://datatofish.com/convert-pandas-dataframe-to-list/ import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
print (products_list)
# [['Product', 'Price'], ['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
データフォーマット
import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
f = '{:<8}|{:<15}' # formatting
for i in products_list:
print(f.format(*i))
Product |Price
Tablet |250
iPhone |800
Laptop |1200
Monitor |300
Reference
この問題について(pandas), 我々は、より多くの情報をここで見つけました
https://velog.io/@ash3767/pandas
テキストは自由に共有またはコピーできます。ただし、このドキュメントのURLは参考URLとして残しておいてください。
Collection and Share based on the CC Protocol
import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
ser_of_bool = df['Age'] > 30
print(df[ser_of_bool])
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
Name Salary Age
0 John 50000 34
1 Sally 120000 45
import pandas as pd
df = pd.read_csv('salaries.csv')
print(df)
print("-"*10)
print(df['Age'])
print(df['Age'].unique())
Name Salary Age
0 John 50000 34
1 Sally 120000 45
2 Alyssa 80000 27
----------
0 34
1 45
2 27
Name: Age, dtype: int64
[34 45 27]
https://www.opentutorials.org/module/3873/23171 特定のリストのみ出力 import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = df.values.tolist()
print (products_list)
# [['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
column names
https://datatofish.com/convert-pandas-dataframe-to-list/ import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
print (products_list)
# [['Product', 'Price'], ['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
データフォーマット
import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
f = '{:<8}|{:<15}' # formatting
for i in products_list:
print(f.format(*i))
Product |Price
Tablet |250
iPhone |800
Laptop |1200
Monitor |300
Reference
この問題について(pandas), 我々は、より多くの情報をここで見つけました
https://velog.io/@ash3767/pandas
テキストは自由に共有またはコピーできます。ただし、このドキュメントのURLは参考URLとして残しておいてください。
Collection and Share based on the CC Protocol
import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = df.values.tolist()
print (products_list)
# [['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
print (products_list)
# [['Product', 'Price'], ['Tablet', 250], ['iPhone', 800], ['Laptop', 1200], ['Monitor', 300]]
import pandas as pd
products = {'Product': ['Tablet','iPhone','Laptop','Monitor'],
'Price': [250,800,1200,300]
}
df = pd.DataFrame(products, columns= ['Product', 'Price'])
products_list = [df.columns.values.tolist()] + df.values.tolist()
f = '{:<8}|{:<15}' # formatting
for i in products_list:
print(f.format(*i))
Product |Price
Tablet |250
iPhone |800
Laptop |1200
Monitor |300
Reference
この問題について(pandas), 我々は、より多くの情報をここで見つけました https://velog.io/@ash3767/pandasテキストは自由に共有またはコピーできます。ただし、このドキュメントのURLは参考URLとして残しておいてください。
Collection and Share based on the CC Protocol