http://pythonstudy.xyz/python/article/408-pandas-%EB%8D%B0%EC%9D%B4%ED%83%80-%EB%B6%84%EC%84%9D

start

pip install pandas

판다스는 데이터 분석을 위해 널리 사용되는 파이썬 라이브러리 패키지이다.

Name,Salary,Age
John,50000,34
Sally,120000,45
Alyssa,80000,27

csv 파일은 준비한다.

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

이라고 출력이 된다.

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

해당 row 만 출력

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

unique() 리스트 출력

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

특정 목록만 출력

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]]

데이터 formatting

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