Pandas DataFrame Plot line graph

Pandas plot

Pandas.DataFrame.plot to get line graphs using data

Python Pandas Plot Line graph by using DataFrame from Excel file with options & to save as image

Let us create a DataFrame with name of the students and their marks.
import pandas as pd 
my_df = pd.DataFrame(data=my_dict)
Output is here
0  Ravi    20
1  Raju    30
2  Alex    40
3   Ron    30
4  Geek    40
5   Kim    50

Line Graph

DataFrame line graph
import pandas as pd 
df = pd.DataFrame(data=my_dict)
df.plot.line(title="Std Mark");


There are several options we can add to above plot. We will discuss them here. Before that let us try some of the options for our line graph.
import pandas as pd 
df = pd.DataFrame(data=my_dict)
df.plot.line( title="Student Mark",figsize=(6,3),x='NAME',y='MARK',
DataFrame line graph with options

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='ENGLISH' to plot the graph against the name of students ( x='NAME' )
import pandas as pd 
df = pd.DataFrame(data=my_dict)
df.plot.line( title="Student Mark",x='NAME',y='ENGLISH',use_index=True,legend=True)


True or False ( default is True ) , To show index or not. 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


This is a list or dict. We can specify how the style of the line.
df.plot.line( title="style =[ ':'] ",x='NAME',style=my_style)
Style Style

logx logy loglog

option loglog=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="loglog=True",figsize=(5,3),loglog=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

df.plot.line( 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

df.plot.line( title="rot=180",x='NAME',figsize=(5,3),rot=180)
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