Different plotting using pandas and matplotlib

We have different types of plots in matplotlib library which can help us to make a suitable graph as you needed. As per the given data, we can make a lot of graph and with the help of pandas, we can create a dataframe before doing plotting of data. Let’s discuss the different types of plot in matplotlib by using Pandas.

Use these commands to install matplotlib, pandas and numpy: 

pip install matplotlib
pip install pandas
pip install numpy 

Types of Plots:

• Basic plotting: In this basic plot we can use the randomly generated data to plot graph using series and matplotlib.

Python3

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

ts = pd.Series(np.random.randn(1000), index = pd.date_range(
                                '1/1/2000', periods = 1000))
ts = ts.cumsum()
ts.plot()

plt.show()

Output:

• Plot of different data: Using more than one list of data in a plot.

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

ts = pd.Series(np.random.randn(1000), index = pd.date_range(
                                '1/1/2000', periods = 1000))

df = pd.DataFrame(np.random.randn(1000, 4),
   index = ts.index, columns = list('ABCD'))

df = df.cumsum()
plt.figure()
df.plot()
plt.show()

Output:

• Plot on given axis: We can explicitly define the name of axis and plot the data on the basis of this axis.

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

ts = pd.Series(np.random.randn(1000), index = pd.date_range(
                                '1/1/2000', periods = 1000))

df = pd.DataFrame(np.random.randn(1000, 4), index = ts.index,
                                      columns = list('ABCD'))

df3 = pd.DataFrame(np.random.randn(1000, 2),
               columns =['B', 'C']).cumsum()

df3['A'] = pd.Series(list(range(len(df))))
df3.plot(x ='A', y ='B')
plt.show()

Output:

• Bar plot using matplotlib: Find different types of bar plot to clearly understand the behaviour of given data.

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

ts = pd.Series(np.random.randn(1000), index = pd.date_range(
                                '1/1/2000', periods = 1000))

df = pd.DataFrame(np.random.randn(1000, 4), index = ts.index,
                                      columns = list('ABCD'))

df3 = pd.DataFrame(np.random.randn(1000, 2),
               columns =['B', 'C']).cumsum()

df3['A'] = pd.Series(list(range(len(df))))
df3.iloc[5].plot.bar()
plt.axhline(0, color ='k')

plt.show()

Output:

• Histograms:

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df4 = pd.DataFrame({'a': np.random.randn(1000) + 1,
                    'b': np.random.randn(1000),
                    'c': np.random.randn(1000) - 1},
                           columns =['a', 'b', 'c'])
plt.figure()

df4.plot.hist(alpha = 0.5)
plt.show()

Output:

• Box plot using Series and matplotlib: Use box to plot the data of dataframe.

Python3

# importing libraries
import matplotlib.pyplot as plt
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()
plt.show()

Output:

• Density plot:

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10, 5),
      columns =['A', 'B', 'C', 'D', 'E'])

ser = pd.Series(np.random.randn(1000))
ser.plot.kde()

plt.show()

Output:

• Area plot using matplotlib:

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10, 5),
       columns =['A', 'B', 'C', 'D', 'E'])

df.plot.area()
plt.show()

Output:

• Scatter plot:

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(500, 4),
           columns =['a', 'b', 'c', 'd'])

df.plot.scatter(x ='a', y ='b')
plt.show()

Output:

• Hexagonal Bin Plot: 

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(1000, 2), columns =['a', 'b'])

df['a'] = df['a'] + np.arrange(1000)
df.plot.hexbin(x ='a', y ='b', gridsize = 25)
plt.show()

Output:

• Pie plot:

Python3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

series = pd.Series(3 * np.random.rand(4),
  index =['a', 'b', 'c', 'd'], name ='series')

series.plot.pie(figsize =(4, 4))
plt.show()

Output: