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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npts = 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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npts = 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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npts = 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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npts = 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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npdf4 = 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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npdf = pd.DataFrame(np.random.rand(10, 5), columns =['A', 'B', 'C', 'D', 'E'])df.plot.box()plt.show() |
Output:
- Density plot:
Python3
# importing librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npdf = 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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npdf = pd.DataFrame(np.random.rand(10, 5), columns =['A', 'B', 'C', 'D', 'E'])df.plot.area()plt.show() |
Output:
- Scatter plot:
Python3
# importing librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npdf = 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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npdf = 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 librariesimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npseries = pd.Series(3 * np.random.rand(4), index =['a', 'b', 'c', 'd'], name ='series')series.plot.pie(figsize =(4, 4))plt.show() |
Output:
