转载：
https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/3-1-pd-intro/
Pandas基本介绍：
Pandas基于Numpy构建，带有标签，易于使用。
两个数据结构：
Series
DataFrame
#Series
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
"""
#DataFrame
dates = pd.date_range('20160101',periods=6)
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
print(df)
"""
a b c d
2016-01-01 -0.253065 -2.071051 -0.640515 0.613663
2016-01-02 -1.147178 1.532470 0.989255 -0.499761
2016-01-03 1.221656 -2.390171 1.862914 0.778070
2016-01-04 1.473877 -0.046419 0.610046 0.204672
2016-01-05 -1.584752 -0.700592 1.487264 -1.778293
2016-01-06 0.633675 -1.414157 -0.277066 -0.442545
"""
DataFrame是表格型数据结构，每一列的元素可以使不同值类型。DataFrame既有行索引也有列索引。
print(df['b'])
"""
2016-01-01 -2.071051
2016-01-02 1.532470
2016-01-03 -2.390171
2016-01-04 -0.046419
2016-01-05 -0.700592
2016-01-06 -1.414157
Freq: D, Name: b, dtype: float64
"""
df.index
df.columns
df.values
df.describe()
Pandas选择数据
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])
"""
A B C D
2013-01-01 0 1 2 3
2013-01-02 4 5 6 7
2013-01-03 8 9 10 11
2013-01-04 12 13 14 15
2013-01-05 16 17 18 19
2013-01-06 20 21 22 23
"""
print(df['A'])
print(df.A)
"""
2013-01-01 0
2013-01-02 4
2013-01-03 8
2013-01-04 12
2013-01-05 16
2013-01-06 20
Freq: D, Name: A, dtype: int64
"""
print(df[0:3])
"""
A B C D
2013-01-01 0 1 2 3
2013-01-02 4 5 6 7
2013-01-03 8 9 10 11
"""
print(df['20130102':'20130104'])
"""
A B C D
2013-01-02 4 5 6 7
2013-01-03 8 9 10 11
2013-01-04 12 13 14 15
"""
行标签 loc
loc,主要通过标签名字选择某一行数据， 或者通过选择某行或者所有行（:代表所有行）然后选其中某一列或几列数据。
print(df.loc['20130102'])
"""
A 4
B 5
C 6
D 7
Name: 2013-01-02 00:00:00, dtype: int64
"""
print(df.loc[:,['A','B']])
"""
A B
2013-01-01 0 1
2013-01-02 4 5
2013-01-03 8 9
2013-01-04 12 13
2013-01-05 16 17
2013-01-06 20 21
"""
print(df.loc['20130102',['A','B']])
"""
A 4
B 5
Name: 2013-01-02 00:00:00, dtype: int64
"""
序列标签
print(df.iloc[3,1])
# 13
print(df.iloc[3:5,1:3])
"""
B C
2013-01-04 13 14
2013-01-05 17 18
"""
print(df.iloc[[1,3,5],1:3])
"""
B C
2013-01-02 5 6
2013-01-04 13 14
2013-01-06 21 22
"""
混合标签
print(df.ix[:3, ['A','C']])
"""
A C
2013-01-01 0 2
2013-01-02 4 6
2013-01-03 8 10
"""
Pandas设置值
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])
"""
A B C D
2013-01-01 0 1 2 3
2013-01-02 4 5 6 7
2013-01-03 8 9 10 11
2013-01-04 12 13 14 15
2013-01-05 16 17 18 19
2013-01-06 20 21 22 23
"""
#loc & iloc
df.iloc[2,2] = 1111
df.loc['20130101','B'] = 2222
"""
A B C D
2013-01-01 0 2222 2 3
2013-01-02 4 5 6 7
2013-01-03 8 9 1111 11
2013-01-04 12 13 14 15
2013-01-05 16 17 18 19
2013-01-06 20 21 22 23
"""
#根据条件设置
df.B[df.A>4] = 0
"""
A B C D
2013-01-01 0 2222 2 3
2013-01-02 4 5 6 7
2013-01-03 8 0 1111 11
2013-01-04 12 0 14 15
2013-01-05 16 0 18 19
2013-01-06 20 0 22 23
"""
df['F'] = np.nan
"""
A B C D F
2013-01-01 0 2222 2 3 NaN
2013-01-02 4 5 6 7 NaN
2013-01-03 8 0 1111 11 NaN
2013-01-04 12 0 14 15 NaN
2013-01-05 16 0 18 19 NaN
2013-01-06 20 0 22 23 NaN
"""
df['E'] = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130101',periods=6))
"""
A B C D F E
2013-01-01 0 2222 2 3 NaN 1
2013-01-02 4 5 6 7 NaN 2
2013-01-03 8 0 1111 11 NaN 3
2013-01-04 12 0 14 15 NaN 4
2013-01-05 16 0 18 19 NaN 5
2013-01-06 20 0 22 23 NaN 6
"""
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