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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 −
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