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# pandas.Series.plot.hist

Series.plot.hist(by=None, bins=10, **kwargs)

  Draw one histogram of the DataFrame’s columns.

  A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one `matplotlib.axes.Axes`. This is useful when the DataFrame’s Series are in a similar scale.

  Parameters:
    by str or sequence, optional

      Column in the DataFrame to group by.

    bins int, default 10

      Number of histogram bins to be used.

    **kwargs

      Additional keyword arguments are documented in `DataFrame.plot()`.

  Returns:
    `matplotlib.axes.Axes`

      Return a histogram plot.

  See also

  `DataFrame.hist`

    Draw histograms per DataFrame’s Series.

  `Series.hist`

    Draw a histogram with Series’ data.

  Examples

  When we roll a die 6000 times, we expect to get each value around 1000 times. But when we roll two dice and sum the result, the distribution is going to be quite different. A histogram illustrates those distributions.

      >>> df = pd.DataFrame(np.random.randint(1, 7, 6000), columns=["one"])
      >>> df["two"] = df["one"] + np.random.randint(1, 7, 6000)
      >>> ax = df.plot.hist(bins=12, alpha=0.5)

  A grouped histogram can be generated by providing the parameter by (which can be a column name, or a list of column names):

      >>> age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85]
      >>> df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list})
      >>> ax = df.plot.hist(column=["age"], by="gender", figsize=(10, 8))