pandas做数据分析(四):常用函数

Love The Way You Lie 2022-10-01 01:49 181阅读 0赞

# 一.统计信息 #

## 1.pandas.DataFrame.describe ##

***DataFrame.describe(percentiles=None, include=None, exclude=None)***

> 作用:  
> 生成简要的统计信息,排除NaN值
> 
> 参数:  
> **percentiles :** array-like, 可选,optional  
> The percentiles to include in the output. Should all be in the interval \[0, 1\]. By default percentiles is \[.25, .5, .75\], returning the 25th, 50th, and 75th percentiles.  
> include, exclude : list-like, ‘all’, or None (default)  
> Specify the form of the returned result. Either:  
> None to both (default). The result will include only numeric-typed columns or, if none are, only categorical columns.  
> A list of dtypes or strings to be included/excluded. To select all numeric types use numpy numpy.number. To select categorical objects use type object. See also the select\_dtypes documentation. eg. df.describe(include=\[‘O’\])  
> If include is the string ‘all’, the output column-set will match the input one.  
> Returns:  
> summary: NDFrame of summary statistics  
> See also DataFrame.select\_dtypes  
> Notes

The output DataFrame index depends on the requested dtypes:

For numeric dtypes, it will include: count, mean, std, min, max, and lower, 50, and upper percentiles.

For object dtypes (e.g. timestamps or strings), the index will include the count, unique, most common, and frequency of the most common. Timestamps also include the first and last items.

For mixed dtypes, the index will be the union of the corresponding output types. Non-applicable entries will be filled with NaN. Note that mixed-dtype outputs can only be returned from mixed-dtype inputs and appropriate use of the include/exclude arguments.

If multiple values have the highest count, then the count and most common pair will be arbitrarily chosen from among those with the highest count.

The include, exclude arguments are ignored for Series.

# 三.绘图相关 #

## 1.pandas.DataFrame.hist ##

使用matplotlib来画出DataFrame的**直方图**.有多少个列,就会画出多少个子图.  
***DataFrame.hist(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, \*\*kwds)***

> 参数:  
> data : DataFrame  
> **column** : 字符串或者序列,如果传进去了,就只会画指定的这些列的直方图.  
> by : object, optional  
> If passed, then used to form histograms for separate groups  
> **grid** : 布尔值,默认是True,表示是否显示网格线.  
> **xlabelsize** : int, default None  
> If specified changes the x-axis label size  
> xrot : float, default None  
> rotation of x axis labels  
> ylabelsize : int, default None  
> If specified changes the y-axis label size  
> yrot : float, default None  
> rotation of y axis labels  
> ax : matplotlib axes object, default None  
> sharex : boolean, default True if ax is None else False  
> In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in; Be aware, that passing in both an ax and sharex=True will alter all x axis labels for all subplots in a figure!  
> sharey : boolean, default False  
> In case subplots=True, share y axis and set some y axis labels to invisible  
> figsize : tuple  
> The size of the figure to create in inches by default  
> layout: (optional) a tuple (rows, columns) for the layout of the histograms  
> **bins**: 整形,默认是10.表示在直方图中箱线条的数量.  
> kwds : other plotting keyword arguments  
> To be passed to hist function