#!/usr/bin/env python
# coding: utf-8

# In[12]:


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

s = pd.Series([1,3,5,np.nan,6,8])
s


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# Cteate a DataFrame
dates = pd.date_range('20130101', periods=6)
# dates 
df = pd.DataFrame(np.random.randn(6,4),index = dates,columns=list('ABCD'))
df


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df.dtypes


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df.head(2)


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df.tail(2)


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# Display the index, columns, and the underlying numpy data
df.index
df.columns
df.values


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# 查看描述性统计
df.describe()


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s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
s


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# Return cumulative sum over requested axis.
df.apply(np.cumsum)


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# Concat function
piece = [df[:2],df[2:3],df[3:1]]
pd.concat(piece)


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# Merge function
left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
right = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [4, 5]})
pd.merge(left,right,on='key')


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# Append function
df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
s = df.iloc[3]
print(df)
print(s)
df.append(s, ignore_index=True)


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df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
						'foo', 'bar', 'foo', 'foo'],
						'B' : ['one', 'one', 'two', 'three',
						'two', 'two', 'one', 'three'],
						'C' : np.random.randn(8),
						'D' : np.random.randn(8)})
# Grouping and then applying a function sum to the resulting groups.

print(df)
df.groupby('A').sum()


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# Time Series 
rng = pd.date_range('1/1/2012', periods=100, freq='S')
# print(rng)
ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
# print(ts)
ts.resample('5Min').sum()


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# ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts=pd.Series(np.random.randn(1000),index=pd.date_range('1/1/2000',periods=1000))
ts = ts.cumsum()
ts.plot()