pandas系列之-DataFrame(4)

Operations

There are lots of operations with pandas that will be really useful to you

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()

array([444, 555, 666])

df['col2'].nunique()

3

df['col2'].value_counts()

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)]

newdf

	col1	col2	col3
3	4	444	xyz

Applying Functions

def times2(x):
    return x*2

df['col1'].apply(times2)

0    2
1    4
2    6
3    8
Name: col1, dtype: int64

df['col3'].apply(len)

0    3
1    3
2    3
3    3
Name: col3, dtype: int64

df['col1'].sum()

10

** Permanently Removing a Column**

del df['col1']

df

	col2	col3
0	444	abc
1	555	def
2	666	ghi
3	444	xyz

** Get column and index names: **

df.columns

Index(['col2', 'col3'], dtype='object')

df.index

RangeIndex(start=0, stop=4, step=1)

** Sorting and Ordering a DataFrame:**

df

	col2	col3
0	444	abc
1	555	def
2	666	ghi
3	444	xyz

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()

	col2	col3
0	False	False
1	False	False
2	False	False
3	False	False

# Drop rows with NaN Values
df.dropna()

	col2	col3
0	444	abc
1	555	def
2	666	ghi
3	444	xyz

** 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()

	col1	col2	col3
0	1.0	NaN	abc
1	2.0	555.0	def
2	3.0	666.0	ghi
3	NaN	444.0	xyz

df.fillna('FILL')

	col1	col2	col3
0	1	FILL	abc
1	2	555	def
2	3	666	ghi
3	FILL	444	xyz

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

	A	B	C	D
0	foo	one	x	1
1	foo	one	y	3
2	foo	two	x	2
3	bar	two	y	5
4	bar	one	x	4
5	bar	one	y	1

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

	C	x	y
A	B
bar	one	4.0	1.0
	two	NaN	5.0
foo	one	1.0	3.0
	two	2.0	NaN