Plot all pandas dataframe columns separately

Question

I have a pandas dataframe who just has numeric columns, and I am trying to create a separate histogram for all the features

ind group people value value_50
 1      1    5    100    1
 1      2    2    90     1
 2      1    10   80     1
 2      2    20   40     0
 3      1    7    10     0
 3      2    23   30     0

but in my real life data there are 50+ columns, how can I create a separate plot for all of them

I have tried

df.plot.hist( subplots = True, grid = True)

It gave me an overlapping unclear plot.

how can I arrange them using pandas subplots = True. Below example can help me to get graphs in (2,2) grid for four columns. But its a long method for all 50 columns

fig, [(ax1,ax2),(ax3,ax4)]  = plt.subplots(2,2, figsize = (20,10))

Answer 1

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)

Answer 2

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])

Answer 3

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()