Let's imagine what we could not plot with Pandas

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import pandas as pd,numpy as np,pylab as pl

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colors=['#3636ff','#ff3636','#36ff36',

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        '#ff36ff','#ffff36','#36ffff']

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user='https://raw.githubusercontent.com/OlgaBelitskaya/'

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path='machine_learning_engineer_nd009/'+\

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     'master/Machine_Learning_Engineer_ND_P3/'

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data=pd.read_csv(user+path+'customers.csv')

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fig=pl.figure(figsize=(6,6))

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ax1=fig.add_subplot(2,1,1)

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params={'frame':data,'class_column':'Channel',

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        'lw':.5,'color':colors}

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pd.plotting.andrews_curves(**params,ax=ax1)

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ax2=fig.add_subplot(2,1,2)

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pd.plotting.parallel_coordinates(**params,ax=ax2)

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pl.show()

Language: Sage Gap GP HTML Macaulay2 Maxima Octave Python R Singular

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Messages

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pl.figure(figsize=(6,4))

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pd.plotting.radviz(**params,s=3)

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pl.xticks([]); pl.yticks([]); pl.show()

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data[data<5000].drop('Region',axis=1)\

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.boxplot(by='Channel',figsize=(6,6),

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         boxprops={'color':colors[0]},

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         flierprops={'markerfacecolor':colors[1]});

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pl.show()

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Messages

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data[data<10000].plot.hexbin(

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    x='Fresh',y='Milk',C='Grocery',

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    reduce_C_function=np.mean,gridsize=20,

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    cmap='Spectral',figsize=(6,4))

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pl.show()

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fig=pl.figure(figsize=(6,4))

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ax=fig.add_subplot(1,1,1)

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data.iloc[:,int(1):].plot.area(

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    stacked=False,ax=ax,cmap='Spectral')

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data_mean=pd.DataFrame(

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    data.iloc[:,int(1):].mean().round()).T

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data_mean.index=['mean']

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pd.plotting.table(ax,data_mean,loc='top')

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pl.grid(); pl.show()

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Messages

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data0=data.drop('Region',axis=1).groupby('Channel').sum().T

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data0.columns=['Channel 1','Channel 2']

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data0.plot.pie(

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    subplots=True,layout=(2,1),figsize=(3,6),legend=False,cmap='bwr')

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pl.show()

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Messages

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data_range=data.iloc[:,int(2):]\

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[(data<5000.)&(data>1000.)]

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params={'bins':int(80),'alpha':.5,

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        'figsize':(6,4),'color':colors}

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data_range.iloc[:,:int(3)].plot.hist(**params)

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pl.show()

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data_range.iloc[:,int(3):].plot.hist(**params)

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pl.show()

Language: Sage Gap GP HTML Macaulay2 Maxima Octave Python R Singular

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Messages