Pandas use

import pandas as pd
import numpy as np
'''
s = pd.Series([1,3,6,np.nan,44,1])
print(s)
0     1.0
1     3.0
2     6.0
3 NaN
4    44.0
5     1.0
dtype: float64
'''
'''
dates = pd.date_range('20160101',periods=6)
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
print(df)
'''
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])
print(df)
print('-------------')
print(df.A) #print(da['A'])

print('-------------')
print(df[0:3])  #print(df['20130101':'20130104'])

#Data selection
#pure tag filter
print(df.loc['20130102']) #Select the line of data 20130102
print(df.loc[:,['A','B']]) #Select: all rows AB and two columns
print(df.loc['20130102',['A','B']])

#pure number filter
print(df.iloc[3]) #Show the index is the third
print(df.iloc[3,1]) #Display the second number in the third line (the subscript starts from 0)
print(df.iloc[1:3,1:3]) #Slice display

#mix filter
print(df.ix[:3,['A','B']]) #0 to 3 rows AB and two columns

# filter selection
print(df[df.A>8]) #Select all rows whose column A is greater than 8