Python Pandas - Visuapzation

Basic Plotting: plot

This functionapty on Series and DataFrame is just a simple wrapper around the matplotpb pbraries plot() method.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range( 1/1/2000 ,
   periods=10), columns=pst( ABCD ))

df.plot()

Its output is as follows −

If the index consists of dates, it calls gct().autofmt_xdate() to format the x-axis as shown in the above illustration.

We can plot one column versus another using the x and y keywords.

Plotting methods allow a handful of plot styles other than the default pne plot. These methods can be provided as the kind keyword argument to plot(). These include −

bar or barh for bar plots

hist for histogram

box for boxplot

area for area plots

scatter for scatter plots

Bar Plot

Let us now see what a Bar Plot is by creating one. A bar plot can be created in the following way −

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10,4),columns=[ a , b , c , d )
df.plot.bar()

Its output is as follows −

To produce a stacked bar plot, pass stacked=True −

import pandas as pd
df = pd.DataFrame(np.random.rand(10,4),columns=[ a , b , c , d )
df.plot.bar(stacked=True)

Its output is as follows −

To get horizontal bar plots, use the barh method −

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10,4),columns=[ a , b , c , d )

df.plot.barh(stacked=True)

Its output is as follows −

Histograms

Histograms can be plotted using the plot.hist() method. We can specify number of bins.

import pandas as pd
import numpy as np

df = pd.DataFrame({ a :np.random.randn(1000)+1, b :np.random.randn(1000), c :
np.random.randn(1000) - 1}, columns=[ a ,  b ,  c ])

df.plot.hist(bins=20)

Its output is as follows −

To plot different histograms for each column, use the following code −

import pandas as pd
import numpy as np

df=pd.DataFrame({ a :np.random.randn(1000)+1, b :np.random.randn(1000), c :
np.random.randn(1000) - 1}, columns=[ a ,  b ,  c ])

df.diff.hist(bins=20)

Its output is as follows −

Box Plots

Boxplot can be drawn calpng Series.box.plot() and DataFrame.box.plot(), or DataFrame.boxplot() to visuapze the distribution of values within each column.

For instance, here is a boxplot representing five trials of 10 observations of a uniform random variable on [0,1).

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10, 5), columns=[ A ,  B ,  C ,  D ,  E ])
df.plot.box()

Its output is as follows −

Area Plot

Area plot can be created using the Series.plot.area() or the DataFrame.plot.area() methods.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10, 4), columns=[ a ,  b ,  c ,  d ])
df.plot.area()

Its output is as follows −

Scatter Plot

Scatter plot can be created using the DataFrame.plot.scatter() methods.

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(50, 4), columns=[ a ,  b ,  c ,  d ])
df.plot.scatter(x= a , y= b )

Its output is as follows −

Pie Chart

Pie chart can be created using the DataFrame.plot.pie() method.

import pandas as pd
import numpy as np

df = pd.DataFrame(3 * np.random.rand(4), index=[ a ,  b ,  c ,  d ], columns=[ x ])
df.plot.pie(subplots=True)

Its output is as follows −