```python
import pandas as pd
import numpy as np
``` ```python
s = pd.Series([1,3,6,np.nan,44,1])
s
------------
0 1.0
1 3.0
2 6.0
3 NaN
4 44.0
5 1.0
dtype: float64 ``` ```python
dates = pd.date_range('',periods=6)
dates
------------
DatetimeIndex(['2019-12-17', '2019-12-18', '2019-12-19', '2019-12-20',
'2019-12-21', '2019-12-22'],
dtype='datetime64[ns]', freq='D')
``` ```python
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
df
-----------------
a b c d
2019-12-17 -0.148546 -0.693356 0.121830 -0.373264
2019-12-18 1.120692 1.023919 1.860231 -0.604606
2019-12-19 0.628022 -1.118451 2.173190 -1.822923
2019-12-20 0.133966 -1.366570 1.076824 0.395217
2019-12-21 1.433090 -1.166129 1.398020 0.118124
2019-12-22 0.707654 1.716963 -2.136076 0.758152
``` ```python
dates = pd.date_range('',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D'])
df
----------------------------
A B C D
2019-12-17 0 1 2 3
2019-12-18 4 5 6 7
2019-12-19 8 9 10 11
2019-12-20 12 13 14 15
2019-12-21 16 17 18 19
2019-12-22 20 21 22 23
``` ```python
print(df['A'])
2019-12-17 0
2019-12-18 4
2019-12-19 8
2019-12-20 12
2019-12-21 16
2019-12-22 20
Freq: D, Name: A, dtype: int32
``` ```python
print(df[0:3])
-----------------------
A B C D
2019-12-17 0 1 2 3
2019-12-18 4 5 6 7
2019-12-19 8 9 10 11
``` ```python
print(df.loc['2019-12-17'])
----------------------
A 0
B 1
C 2
D 3
Name: 2019-12-17 00:00:00, dtype: int32
``` ```python
print(df.loc[:,['A','B']])
-------------------------
A B
2019-12-17 0 1
2019-12-18 4 5
2019-12-19 8 9
2019-12-20 12 13
2019-12-21 16 17
2019-12-22 20 21
``` ```python
print(df.iloc[3])
----------------------
A 12
B 13
C 14
D 15
Name: 2019-12-20 00:00:00, dtype: int32
``` ```python
print(df.iloc[3,1])
---------------
13
``` ```python
print(df.iloc[3:5,1:3])
---------------------
B C
2019-12-20 13 14
2019-12-21 17 18
``` ```python
print(df.iloc[[1,3,5],1:3])
-----------------------
B C
2019-12-18 5 6
2019-12-20 13 14
2019-12-22 21 22
``` ```python
print(df[df.A>8])
------------------
A B C D
2019-12-20 12 13 14 15
2019-12-21 16 17 18 19
2019-12-22 20 21 22 23
``` ```python
df.iloc[2,2] = 1111
print(df)
----------------------
A B C D
2019-12-17 0 1 2 3
2019-12-18 4 5 6 7
2019-12-19 8 9 1111 11
2019-12-20 12 13 14 15
2019-12-21 16 17 18 19
2019-12-22 20 21 22 23
``` ```python
df.loc['2019-12-17','B'] = 222
df
----------------------
A B C D
2019-12-17 0 222 2 3
2019-12-18 4 5 6 7
2019-12-19 8 9 1111 11
2019-12-20 12 13 14 15
2019-12-21 16 17 18 19
2019-12-22 20 21 22 23
``` ```python
df.A[df.A>4] = 0
df
--------------- A B C D
2019-12-17 0 222 2 3
2019-12-18 4 5 6 7
2019-12-19 0 9 1111 11
2019-12-20 0 13 14 15
2019-12-21 0 17 18 19
2019-12-22 0 21 22 23
``` ```python
df['E'] = pd.Series([1,2,3,4,5,6],index=pd.date_range('',periods=6))
df
--------------------- A B C D E
2019-12-17 0 0 2 3 1
2019-12-18 4 0 6 7 2
2019-12-19 0 0 1111 11 3
2019-12-20 0 0 14 15 4
2019-12-21 0 0 18 19 5
2019-12-22 0 0 22 23 6
``` ```python
df.iloc[0,1] = np.nan
df.iloc[1,2] = np.nan
df
---------------
A B C D E
2019-12-17 0 NaN 2.0 3 1
2019-12-18 4 0.0 NaN 7 2
2019-12-19 0 0.0 1111.0 11 3
2019-12-20 0 0.0 14.0 15 4
2019-12-21 0 0.0 18.0 19 5
2019-12-22 0 0.0 22.0 23 6
``` ```python
df.dropna(axis=0,how='any') # how=['any','all']
------------------
A B C D E
2019-12-19 0 0.0 1111.0 11 3
2019-12-20 0 0.0 14.0 15 4
2019-12-21 0 0.0 18.0 19 5
2019-12-22 0 0.0 22.0 23 6
``` ```python
df.iloc[1,1] = np.nan
df
----------------- A B C D E
2019-12-17 0 NaN 2.0 3 1
2019-12-18 4 NaN NaN 7 2
2019-12-19 0 0.0 1111.0 11 3
2019-12-20 0 0.0 14.0 15 4
2019-12-21 0 0.0 18.0 19 5
2019-12-22 0 0.0 22.0 23 6
``` ```python
print(df.fillna(value=12))
------------------
A B C D E
2019-12-17 0 12.0 2.0 3 1
2019-12-18 4 12.0 12.0 7 2
2019-12-19 0 0.0 1111.0 11 3
2019-12-20 0 0.0 14.0 15 4
2019-12-21 0 0.0 18.0 19 5
2019-12-22 0 0.0 22.0 23 6
``` ```python
print(df.isnull())
---------------------
A B C D E
2019-12-17 False True False False False
2019-12-18 False True True False False
2019-12-19 False False False False False
2019-12-20 False False False False False
2019-12-21 False False False False False
2019-12-22 False False False False False
``` ```python
df1 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2,columns=['a','b','c','d'])
print(df1)
print(df2)
print(df3)
-----------------------
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
a b c d
0 1.0 1.0 1.0 1.0
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
a b c d
0 2.0 2.0 2.0 2.0
1 2.0 2.0 2.0 2.0
2 2.0 2.0 2.0 2.0
``` ```python
res = pd.concat([df1,df2,df3],ignore_index=True)
res
--------------------
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
6 2.0 2.0 2.0 2.0
7 2.0 2.0 2.0 2.0
8 2.0 2.0 2.0 2.0
``` ```python
df4 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d'])
df5 = pd.DataFrame(np.ones((3,4))*1,columns=['b','c','d','e'])
print(df4)
print(df5)
-----------------
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
b c d e
0 1.0 1.0 1.0 1.0
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
``` ```python
res = pd.concat([df4,df5],sort=True)
res
--------------------- a b c d e
0 0.0 0.0 0.0 0.0 NaN
1 0.0 0.0 0.0 0.0 NaN
2 0.0 0.0 0.0 0.0 NaN
0 NaN 1.0 1.0 1.0 1.0
1 NaN 1.0 1.0 1.0 1.0
2 NaN 1.0 1.0 1.0 1.0
``` ```python
res = pd.concat([df4,df5],axis=1)
res
----------------
a b c d b c d e
0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
1 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
``` ```python
df8 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d'])
s1 = pd.Series([1,2,3,4],index=['a','b','c','d'])
res = df8.append(s1,ignore_index=True)
res
---------------
``` ```python
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 2.0 3.0 4.0
```