Python数据分析模块 | pandas做数据分析(三):统计相关函数

计算操作

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)

最大最小值

标准统计函数

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:

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:

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