pandas做数据分析(五):统计相关函数

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:

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:

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