Plot all pandas dataframe columns separately

Pandas subplots=True will arange the axes in a single column.

import numpy as npimport matplotlib.pyplot as pltimport pandas as pddf = pd.DataFrame(np.random.rand(7,20))df.plot(subplots=True)plt.tight_layout()plt.show()

Here, tight_layout isn't applied, because the figure is too small to arange the axes nicely. One can use a bigger figure (figsize=(...)) though.

In order to have the axes on a grid, one can use the layout parameter, e.g.

df.plot(subplots=True, layout=(4,5))

The same can be achieved if creating the axes via plt.subplots()

fig, axes = plt.subplots(nrows=4, ncols=5)df.plot(subplots=True, ax=axes)

If you want to plot them separately (which is why I ended up here), you can use

for i in df.columns:    plt.figure()    plt.hist(df[i])

An alternative for this task can be using the "hist" method with hyperparameter "layout". Example using part of the code provided by @ImportanceOfBeingErnest:

import numpy as npimport matplotlib.pyplot as pltimport pandas  as pddf = pd.DataFrame(np.random.rand(7,20))df.hist(layout=(5,4), figsize=(15,10))plt.show()