pandas做数据分析(五):统计相关函数

痛定思痛。 2022-10-01 01:51 316阅读 0赞

# 一.计数操作 #

## 1.pandas.Series.value\_counts ##

***Series.value\_counts(normalize=False,sort=True,ascending=False, bins=None, dropna=True)***

> 作用:返回一个**包含值和该值出现次数**的Series对象,次序按照出现的频率由高到低排序.  
> 参数:  
> **normalize** : 布尔值,默认为False,如果是True的话,就会包含该值出现次数的频率.  
> **sort** : 布尔值,默认为True.排序控制.  
> **ascending** : 布尔值,默认为False,以升序排序  
> **bins** : integer, optional  
> Rather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data  
> **dropna** : 布尔型,默认为True,表示不包括NaN

## 2.pandas.DataFrame.count ##

***DataFrame.count(axis=0, level=None, numeric\_only=False)***  
Return Series with number of non-NA/null observations over requested axis. Works with non-floating point data as well (detects NaN and None)

Parameters:  
axis : \{0 or ‘index’, 1 or ‘columns’\}, default 0  
0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise  
level : int or level name, default None  
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame  
numeric\_only : boolean, default False  
Include only float, int, boolean data  
Returns:  
count : Series (or DataFrame if level specified)

# 二.最大最小值 #

# 三.标准统计函数 #

## 1.pandas.DataFrame.sum ##

返回指定轴上值的和.

***DataFrame.sum(axis=None, skipna=None, level=None, numeric\_only=None, \*\*kwargs)***

> 参数:  
> **axis** : \{index (0), columns (1)\}  
> **skipna** : 布尔值,默认为True.表示跳过NaN值.如果整行/列都是NaN,那么结果也就是NaN  
> **level** : int or level name, default None  
> If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series  
> **numeric\_only** : boolean, default None  
> Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.  
> Returns:  
> sum : Series or DataFrame (if level specified)

import numpy as np
    import pandas as pd
    
    df=pd.DataFrame(data=[[1.4,np.nan],[7.1,-4.5],[np.nan,np.nan],[0.75,-1.3]],
                    index=["a","b","c","d"],
                    columns=["one","two"])
    print("df:")
    print(df)
    
    #直接使用sum()方法,返回一个列求和的Series,自动跳过NaN值
    print("df.sum()")
    print(df.sum())
    
    #当轴为1.就会按行求和
    print("df.sum(axis=1)")
    print(df.sum(axis=1))
    
    #选择skipna=False可以禁用跳过Nan值
    print("df.sum(axis=1,skipna=False):")
    print(df.sum(axis=1,skipna=False))

结果:  
![这里写图片描述][SouthEast]

## 2.pandas.DataFrame.mean ##

**返回指定轴上值的平均数.**

***DataFrame.mean(axis=None,skipna=None,level=None,numeric\_only=None, \*\*kwargs)***

> 参数:  
> **axis** : \{index (0), columns (1)\}  
> **skipna** :布尔值,默认为True.表示跳过NaN值.如果整行/列都是NaN,那么结果也就是NaN  
> **level** : int or level name, default None  
> If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series  
> **numeric\_only** : boolean, default None  
> Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

例子:

import numpy as np
    import pandas as pd
    
    df=pd.DataFrame(data=[[1.4,np.nan],[7.1,-4.5],[np.nan,np.nan],[0.75,-1.3]],
                    index=["a","b","c","d"],
                    columns=["one","two"])
    print("df:")
    print(df)
    
    #直接使用mean()方法,返回一个列求平均数的Series,自动跳过NaN值
    print("df.mean()")
    print(df.mean())
    
    #当轴为1.就会按行求平均数
    print("df.mean(axis=1)")
    print(df.mean(axis=1))
    
    #选择skipna=False可以禁用跳过Nan值
    print("df.mean(axis=1,skipna=False):")
    print(df.mean(axis=1,skipna=False))

结果:  
![这里写图片描述][SouthEast 1]

# 四.排序 #

## 1.pandas.DataFrame.sort\_values ##

***`DataFrame.sort_values(by,axis=0,ascending=True,inplace=False, kind='quicksort', na_position='last')`***  
Sort by the values along either axis

> 参数:  
> by : str or list of str  
> Name or list of names which refer to the axis items.  
> axis : \{0 or ‘index’, 1 or ‘columns’\}, default 0  
> Axis to direct sorting  
> ascending : bool or list of bool, default True  
> Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.  
> inplace : bool, default False  
> if True, perform operation in-place  
> kind : \{‘quicksort’, ‘mergesort’, ‘heapsort’\}, default ‘quicksort’  
> Choice of sorting algorithm. See also ndarray.np.sort for more information. mergesort is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label.  
> na\_position : \{‘first’, ‘last’\}, default ‘last’  
> first puts NaNs at the beginning, last puts NaNs at the end  
> Returns:  
> sorted\_obj : DataFrame

[SouthEast]: /images/20220705/c556deb004b34ea2b181e955ae381a94.png
[SouthEast 1]: /images/20220705/022c659e60b5458ba9fad25a34c38356.png