TermsThe 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()
| Series | - dataframe.plot(x='None',y='None')
| DataFrame | - data.plot.<kind>()
| Another method | 'bar' or 'barh', 'hist', 'box', 'kde' or 'density' , 'hexbin', 'pie' and 'scatter'
| Kinds |
Bar Plot- data.plot.bar() /.barh()
| Bar plot/ Horizontal plot | - data.plot.bar(stacked=True)
| Stacked bar plot |
Area Plots- data.plot.area()
| Area plot | - data.plot.area(stacked=False)
| Non-Stacked area plot |
Pie plot- series.plot.pie()
| Pie plot for Series | - DataFrame.plot.pie(subplots=True)
| Pie plot for DataFrame | - series.plot.pie(labels= ['A','B','C'], colors= ['r','b','g'], autopct= '%.2f')
| Wedge labels |
It's valid:
fontsize and figsize Scatter plot-DataFrame.plot.scatter(x=' ', y=' ')
| Scatter plot | -ax= df.plot.scatter(x='A',y='B', color='None', label='Group1')
| -df.plot.scatter(x='C',y='D', color='Other', label='Group2', ax= ax)
| Multiple plot |
| | Histograms- data.plot.hist()
| Histogram plot | - data.plot.hist(stacked=True, bins=10)
| Stacked and bins size | - data.plot.hist(orientation='horizontal', cumulative=True)
| Horizontal and cumulative | data.diff().hist(color='g', alpha=0.5)
| Subplots histograms |
Box Plots- data.plot.box()
| Box plot | - dict={'boxes':' ','whiskers':' ', 'medians':' ', 'caps': ' '}
| Color of Boxes | - data.plot.box(color=dict)
| - data.plot.box(vert=False)
| Horizontal box plot | - df.boxplot(by='column')
| - df.boxplot(column=[' ',' '], by=[' ',' ']))
| Groupings | - df.groupby('g').boxplot()
| 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')
| Hexagonal bin plot | - DataFrame.plot.hexbin(x='None', y='None', C= 'z', reduce_C_function=np.max)
| add column 'z' for the value | - DataFrame.plot.hexbin(x='None', y='None', gridsize= 20)
| Gridsize |
Density plot- data.plot.kde()
| 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 formattingPlot 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)
| Subplots | - data.plot(subplots= True, layout= (2,3)
| Multiple axes |
It´s valid:
figsize and sharex Plotting with errors barsDataFrame.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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