计算 pandas 列中条目的频率，然后使用 X 轴字符串 labell 将它们绘制成图

Question

我有以下熊猫专栏：

FuncGroup

ABC

ABC

ABC

ABC

BCD

BCD

BCD

SDS

SDS

ABC

BCD

SDS

BCD

我想在熊猫数据框中获得这个预期的输出：

pd['FunctionGroup','FunctionCount']

ABC  4

BCD  5

SDS  3

如何做到这一点，它是图形目的所必需的。

编辑 1：通过参考以下答案，我对原始代码进行了一些修改以使用 plotly 进行绘图。现在绘制了所有计数，但 X 轴标签不会使用此方法出现，这就是我希望将标签和计数存储在 pd 中的原因。

参考代码

otrace1 =go.Bar(

    #x=stock_opt_pe.index

    x=datalist['Function group'].nunique(),

    y=datalist['Function group'].value_counts(),

    text=datalistFg, # dont know what to give here to get a X axis label

    textposition = 'auto',

    #xaxis-type (enumerated: “-” | “linear” | “log” | “date” | “category” )

    #xaxis-type (enumerated: “-” | “linear” | “log” | “date” | “category” )

    #name='Function Group Vx RespPerson',

    #orientation = 'v',

    #marker = dict(

        #color = 'rgba(224, 224, 224, 0.6)',

        #line = dict(

            #color = 'rgba(246, 250, 206, 1.0)',

            #color = 'rgb(60, 60, 60)',

            #width = 0)

    #)

)

Answer 1

检查这是否适合您：import pandas as pdimport plotly.plotly as py示例 df：raw =pd.DataFrame({'FuncGroup':['ABC','ABC','ABC','ABC','BCD','BCD','BCD','SDS','SDS','ABC','BCD','SDS','BCD']})使用计数创建新的 df：s = raw['FuncGroup'].value_counts() ## Counts the occurrence of unqiue elements and stores in a variable called "s" which is series typenew = pd.DataFrame({'FuncGroup':s.index, 'Count':s.values})  ## Converting series type to pandas df as plotly accepts dataframe as input. The two columns of df is FuncGroup which is being made by index of series and new variable called count which is made by values of series s.创建绘图条形图：py.iplot(new, filename='basic-bar')

Answer 2

您可能正在寻找类似于collections counter.df['FuncGroup'].value_counts()对于绘图，请看这个例子：import pandas as pddf = pd.DataFrame({    'FuncGroup': ['ABC','ABC','BCD']})s = df['FuncGroup'].value_counts()s.plot(kind='bar')dfout = df['FuncGroup'].value_counts().reset_index()print(dfout)#  index  FuncGroup#0   ABC          2#1   BCD          1返回：