Terms
The terms will be used to refer to:
- df = Pandas DataFrame
- series = Pandas Series
- data = Pandas DataFrame or Series |
Plot whith Series and DataFrames
- series.plot()
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Series |
- dataframe.plot(x='None',y='None')
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DataFrame |
- data.plot.<kind>()
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Another method |
'bar' or 'barh', 'hist', 'box', 'kde' or 'density' , 'hexbin', 'pie' and 'scatter'
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Kinds |
Bar Plot
- data.plot.bar() /.barh()
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Bar plot/ Horizontal plot |
- data.plot.bar(stacked=True)
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Stacked bar plot |
Area Plots
- data.plot.area()
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Area plot |
- data.plot.area(stacked=False)
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Non-Stacked area plot |
Pie plot
- series.plot.pie()
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Pie plot for Series |
- DataFrame.plot.pie(subplots=True)
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Pie plot for DataFrame |
- series.plot.pie(labels= ['A','B','C'], colors= ['r','b','g'], autopct= '%.2f')
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Wedge labels |
It's valid:
fontsize and figsize
Scatter plot
-DataFrame.plot.scatter(x=' ', y=' ')
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Scatter plot |
-ax= df.plot.scatter(x='A',y='B', color='None', label='Group1')
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-df.plot.scatter(x='C',y='D', color='Other', label='Group2', ax= ax)
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Multiple plot |
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Histograms
- data.plot.hist()
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Histogram plot |
- data.plot.hist(stacked=True, bins=10)
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Stacked and bins size |
- data.plot.hist(orientation='horizontal', cumulative=True)
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Horizontal and cumulative |
data.diff().hist(color='g', alpha=0.5)
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Subplots histograms |
Box Plots
- data.plot.box()
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Box plot |
- dict={'boxes':' ','whiskers':' ', 'medians':' ', 'caps': ' '}
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Color of Boxes |
- data.plot.box(color=dict)
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- data.plot.box(vert=False)
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Horizontal box plot |
- df.boxplot(by='column')
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- df.boxplot(column=[' ',' '], by=[' ',' ']))
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Groupings |
- df.groupby('g').boxplot()
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for random choice |
The "choice random" is:
- g=np.random_choice(['A','B'],size=50])
Hexagonal bin plot
- DataFrame.plot.hexbin(x='None', y='None')
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Hexagonal bin plot |
- DataFrame.plot.hexbin(x='None', y='None', C= 'z', reduce_C_function=np.max)
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add column 'z' for the value |
- DataFrame.plot.hexbin(x='None', y='None', gridsize= 20)
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Gridsize |
Density plot
- data.plot.kde()
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Density plot |
Plot for data .CSV
> data= pd.read_csv('Name or direction of data')
Andrews curves
- pdt.andrews_curves(data, 'column name with class names')
Parallel coordinates
- pdt.parallel_coordinates(data, 'column name with class names')
RadViz
- pdt.radviz(data, 'column name with class name')
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Plotting Tools from Pandas Plotting
> import pandas.plotting as pdt
Scatter matrix plot
- pdt.scatter_matrix('frame', 'alpha= 0.5', 'figsize=(6,6)')
Lag plot
- pdt.lag_plot(series)
Autocorrelation plot
- pdt.autocorrelation_plot(series)
Bootstrap plot
- pdt.bootstrap_plot(series, size= 50, samples= 500, color='green')
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Plot formatting
Plot style
- series.plot(style='k--')
Controlling the legend
- DataFrame.plot(legend= False)
Color map
- DataFrame.plot(colormap=' ')
Scales (logarithmic)
- data.plot(logy= True) or logx or loglog
Plotting on a seconday y-axis
- DataFrame.column1.plot()
- DataFrame.column2.plot(secondary_y=True)
Suppressing tick resolution adjustament
- data.plot(x_compat= True) |
Subplots
- data.plot(subplots=True)
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Subplots |
- data.plot(subplots= True, layout= (2,3)
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Multiple axes |
It´s valid:
figsize and sharex
Plotting with errors bars
DataFrame.plot.bar(yerr=df_err, xerr= df1_err, capsize=3)
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df_err and df1_err are DataFrame of the errors of X and Y
Plotting tables
- ax.get_xaxis().set_visible(False)
- DataFrame.plot(table= True, ax=ax)
Adds table to:
- fig, ax= plt.subplots(1,1)
- pdt.table(ax, DataFrame, loc='upper right', colWidths=[0.2, 0.2, 0.2])
- DataFrame.plot(ax= ax)
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