Python Pandas - Visualization
Basic Plotting: plot This functionality on Series and DataFrame is just a simple wrapper around the matplotlib libraries 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=list('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 line 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 calling Series.box.plot() and DataFrame.box.plot(), or DataFrame.boxplot() to visualize 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 −