pandas学习笔记（未完成

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import pandas as pd
import numpy as np

定义序列, pandas中的数据形式通常是float32或float64

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s = pd.Series([1,3,4,np.nan,11,99])
s

0     1.0
1     3.0
2     4.0
3     NaN
4    11.0
5    99.0
dtype: float64

定义日期列表

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dates = pd.date_range('20180927',periods=6)
dates

DatetimeIndex(['2018-09-27', '2018-09-28', '2018-09-29', '2018-09-30',
               '2018-10-01', '2018-10-02'],
              dtype='datetime64[ns]', freq='D')

DataFrame类似于numpy的array

不指定索引的DataFrame

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df = pd.DataFrame(np.arange(12).reshape(3, 4))
>>> df

DataFrame的定义

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>>>df = pd.DataFrame({'A': 1., 'B': 'Foo', 'C': np.array([3] * 4)})
>>>df

行索引为dates, 列索引为[a, b, c, d]

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>>> df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
>>> df

查看DataFrame的数据类型

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>>> df.dtypes

a    float64
b    float64
c    float64
d    float64
dtype: object

查看DataFrame的索引

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>>> df.index

DatetimeIndex(['2018-09-27', '2018-09-28', '2018-09-29', '2018-09-30',
               '2018-10-01', '2018-10-02'],
              dtype='datetime64[ns]', freq='D')

查看DataFrame的列索引

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>>> df.columns

Index(['a', 'b', 'c', 'd'], dtype='object')

查看DataFrame的值

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>>> df.values

array([[ 0.33883139,  1.03657755, -2.5732431 , -2.20443975],
       [-0.47379902, -0.64674734,  0.43553894, -0.75883344],
       [ 0.30546684,  0.81604074, -0.11604421,  1.19749384],
       [-0.43536792, -0.08233739,  0.62448617, -1.23405699],
       [-0.66747791,  0.23327389, -1.38001185,  2.26103083],
       [-0.65425047, -0.18060444,  0.60901542, -0.92445528]])

查看DataFrame的描述

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>>> df.describe()

DataFrame的转置

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>>> df.T

DataFrame的index排序

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>>> df.sort_index(axis=1)

DataFrame的index排序, 逆序

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>>> df.sort_index(axis=1,ascending=False)

DataFrame按值排序

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>>> df.sort_values(by='c')