Plot all pandas dataframe columns separately

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 np
import matplotlib.pyplot as plt
import pandas  as pd

df = pd.DataFrame(np.random.rand(7,20))

df.hist(layout=(5,4), figsize=(15,10))

plt.show()

Using pandas.DataFrame I would suggest using pandas.DataFrame.apply. With a custom function, in this example plot(), you can print and save each figure seperately.

def plot(col):
 
    fig, ax = plt.subplots()
    ax.plot(col)
    plt.show()

df.apply(plot)

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])

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

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

df = 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)