06-Operations

Operations

import pandas as pd
df = pd.DataFrame({'col1':[1,2,3,4],'col2':[444,555,666,444],'col3':['abc','def','ghi','xyz']})
df.head()
'''
	col1	col2	col3

0	1	444	abc
1	2	555	def
2	3	666	ghi
3	4	444	xyz
'''

 

Info on Unique Values

df['col2'].unique()  # 유일한 값 찾기 (중복 제거) 
# array([444, 555, 666])

df['col2'].nunique()   # 유일한 값 인덱스 길이 반환. len(unique())와 같다.
# 3

df['col2'].value_counts()  # 유일한 값 개수 세기 
'''
444    2
555    1
666    1
Name: col2, dtype: int64
'''

 

Selecting Data

#Select from DataFrame using criteria from multiple columns
newdf = df[(df['col1']>2) & (df['col2']==444)]
'''
	col1	col2	col3
3	4	444	xyz
'''

 

Applying Functions

def times2(x):
    return x*2
    
df['col1'].apply(times2)

df['col3'].apply(len)

df['col1'].sum()

 

열 삭제:  del df[ ]

del df['col1']

Get column and index names:    .colums    .index

df.columns # Index(['col2', 'col3'], dtype='object')

df.index  # RangeIndex(start=0, stop=4, step=1)

Sorting and Ordering a DataFrame:  .sort_values()

df.sort_values(by='col2') #inplace=False by default
'''
	col2	col3

0	444	abc
3	444	xyz
1	555	def
2	666	ghi
'''

Find Null Values or Check for Null Values:

df.isnull()

# Drop rows with NaN Values
df.dropna()

 Filling in NaN values with something else: 

import numpy as np

df = pd.DataFrame({'col1':[1,2,3,np.nan],
                   'col2':[np.nan,555,666,444],
                   'col3':['abc','def','ghi','xyz']})
df.head()    # 데이터의 상위 5개의 행을 출력

df.fillna('FILL')  # NAN에 다음 값('FILL')을 채우기 

data = {'A':['foo','foo','foo','bar','bar','bar'],
     'B':['one','one','two','two','one','one'],
       'C':['x','y','x','y','x','y'],
       'D':[1,3,2,5,4,1]}

df = pd.DataFrame(data)

df.pivot_table(values='D',index=['A', 'B'],columns=['C'])  # 피벗 테이블 

 

Great Job!