Python-pandas模块数据处理

谁借莪１个温暖的怀抱￠ 2022-06-16 09:49 321阅读 0赞

# 1.常用数据结构 #

(1)**Series**相当于一个一维数组，只不过多了一个索引

>import pandas as pd
    >s=pd.Series([1,2,3,4],index=['a','b','c','d'])
    >s
    a    1
    b    2
    c    3
    d    4
    dtype: int64
    >s['a']
    1

(2)**DataFrame**相当于一个二维数组，可以通过行，列来索引

>df=pd.DataFrame({
       'key1':['a','a','b','b','a'],'key2':['one','two','one','two','one'],'data1':np.random.randn(5),'data2':np.random.randn(5)})
          data1     data2 key1 key2
    0  0.298132 -0.889997    a  one
    1 -1.610528  0.735897    a  two
    2  1.229059 -0.922434    b  one
    3 -0.419731  1.611932    b  two
    4 -0.485703 -1.041524    a  one
    >df.ix[1,:]
    data1    -1.61053
    data2    0.735897
    key1            a
    key2          two
    Name: 1, dtype: object
    >df['data1']
    0    0.298132
    1   -1.610528
    2    1.229059
    3   -0.419731
    4   -0.485703
    Name: data1, dtype: float64

# 2.常用操作 #

【不定期更新，边学边练】  
**（1）groupby**  
**（2）rolling,rolling\_mean**  
DataFrame.rolling(window, min\_periods=None, freq=None, center=False, win\_type=None, on=None, axis=0, closed=None)  
相当于滑动窗口处理数据  
window–窗口大小  
min\_periods–至少得有多少个元素才能计算结果

>df['data1'].rolling(2).sum()
    #第一个窗口因为只有一个元素，所以没办法计算结果
    0         NaN
    1   -1.312396
    2   -0.381468
    3    0.809328
    4   -0.905435
    Name: data1, dtype: float64
    >df['data1'].rolling(2,min_periods=1).sum()
    #设置min_peroid,第一个窗口也可以成功计算
    0    0.298132
    1   -1.312396
    2   -0.381468
    3    0.809328
    4   -0.905435
    Name: data1, dtype: float64

这个函数好像快被前面那个替代了  
pandas.rolling\_mean(arg, window, min\_periods=None, freq=None, center=False, how=None, \*\*kwargs)

>pd.rolling_mean(df[['data1','data2'],2)
          data1     data2
    0       NaN       NaN
    1 -0.656198 -0.077050
    2 -0.190734 -0.093268
    3  0.404664  0.344749
    4 -0.452717  0.285204
    >pd.rolling_mean(df[['data1','data2']],2,min_periods=1)
          data1     data2
    0  0.298132 -0.889997
    1 -0.656198 -0.077050
    2 -0.190734 -0.093268
    3  0.404664  0.344749
    4 -0.452717  0.285204