Pandas DataFrame Plot bar graph

Pandas plot

Pandas.DataFrame.plot to get bar graphs using data

Let us create a DataFrame with name of the students and their marks. DataFrame bar graph
import pandas as pd 
df = pd.DataFrame(data=my_dict)"Std Mark",

Bar Graph with options

There are several options we can add to above bar graph.

title :

title='Student Mark' String used as Title of the graph. We can also use one list to give titles to sub graphs. ( for this subplot must be true )

figsize :

Size of the graph , it is a tuple saying width and height in inches, figsize=(6,3). Here width is 6 inches and height is 3 inches.

X: Y:

What is to be used in X and Y axis. If you have multiple keys then you can specify what can be used. Note that Y axis must be numeric data to plot the graph.
Example : Here we have used y='MARK' to plot the graph against the name of students ( x='NAME' )"Std Mark",x='NAME',y='MARK',figsize=(6,3));


True or False ( default is True ) , To show index ( use_index = True) or not ( use_index = False). The difference is mentioned here ( check X - axis ) .
use_index=True option use_index=False option


We will show grid ( grid=True ) or not ( grid=False) grid=True option grid=False option


True of False ( default is True ), to show legend ( legend=True ) or not ( legend=False)
legend=True option legend=False option


The colour of the bar we can define by using list. We have to give our inputs in R G B where each value varies from 0 to 1. Here is one sample.
color option code is here .

We can define more colours in combination of RGB and use them. Two color option

We can define different colours to each bar of the graph. We will prepare a list of different colours for each bar.
Two color option
my_colors = [(x/10.0, x/20.0, .9)
        for x in range(len(df))]"color",x='NAME',

logx logy loglog

option logy=True We can specify log scaling or symlog scaling for x ( logx=True ) or y ( logy=True ) or for both x & y ( loglog=True)

df.plot.line( title="logy=True",logy=True)


Whether to plot on secondary Y Axis ( secondary_y=True ) or not ( secondary_y=False )
secondary_y=True secondary_y=False


Check the image above when secondary_y=True. There is a automatic marking in column lebels saying (right). We can manage this to show ( mark_right=True) or not ( mark_right=False)
mark_right=True mark_right=False title="secondary_y=True",x='NAME',figsize=(5,3),secondary_y=True)


Rotation of ticks ( check the names at x axis )
rot=45 rot=180 title="rot=180",x='NAME',figsize=(5,3),rot=180)


We can make bars stacked one over other ( stacked=True ) or not to draw side by side ( stacked=False)
stacked=True stacked=False

import pandas as pd 
df = pd.DataFrame(data=my_dict)"stacked=True",x='NAME',figsize=(5,3),stacked=True)
Pandas plot plot.barh()

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