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))

So it was an example of what you don't want. But what's what you want? How should the figure look like? — Stop harming Monica, Apr 08 '19 at 07:59
@goyo histogram graph for all the columns in a data frame, simple! In the above example, its 5 columns, in my real example it's 50. Can you write something generic? I am sure it can be done — Manu Sharma, Apr 08 '19 at 08:06
It looks like you just need a bigger figure, but you already know how to do that so what is missing? — Stop harming Monica, Apr 08 '19 at 08:12

score 69 · Accepted Answer · answered Apr 08 '19 at 09:59

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)

@ ImportanceOfBeingErnest Could you figure out how to handle X and Y axis scale in each plot? ![Here's the question](https://stackoverflow.com/questions/68141666/how-to-change-the-color-of-individual-histograms-in-dataframe-hist-or-datafram) — Girish Kumar Chandora, Jun 26 '21 at 11:01

score 14 · Answer 2 · answered Aug 29 '19 at 09:25

14

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])

answered Aug 29 '19 at 09:25

annhak

602
5
16

score 10 · Answer 3 · answered Apr 06 '20 at 20:05

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()

score 3 · Answer 4 · answered May 03 '21 at 17:10

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)

wwii · Answer 5 · 2020-08-13T17:16:24.877

While not asked for in the question I thought I'd add that using the x parameter to plot would allow you to specify a column for the x axis data.

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

df = pd.DataFrame(np.random.rand(7,20),columns=list('abcdefghijklmnopqrst'))
df.plot(x='a',subplots=True, layout=(4,5))    

plt.tight_layout()
plt.show()

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html

Plot all pandas dataframe columns separately

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