独家 | 手把手教你怎样用Python生成漂亮且精辟的图像（附教程代码）

1. import json  2. import pandas as pd  3. import numpy as np  4


    
. %matplotlib inline  5. import matplotlib.pyplot as plt  6. import seaborn as sns  7. sns.set(  8.     font_scale=1.5,  9.     style="whitegrid",  10.     rc={'figure.figsize':(20,7)}  11. )  

1. !pip install seaborn

1. sales_team = pd.read_csv('sales_team.csv') 2. order_leads = pd.read_csv('order_leads.csv') 3. invoices = pd.read_csv('invoices.csv')  

1. _ = order_leads.set_index(pd.DatetimeIndex(order_leads.Date)).groupby( 2.     pd.Grouper(freq='D')  3. )['Converted'].mean()  4.   5. ax = _.rolling(60).mean().plot(figsize=(20,7),title='Conversion Rate Over Time')  6.   7. vals = ax.get_yticks() 8. ax.set_yticklabels(['{:,.0f}%'.format(x*100) for x in vals])  9. sns.despine()  

1. orders_with_sales_team = pd.merge(order_leads,sales_team,on=['Company Id','Company Name'])  2. ax = sns.distplot(orders_with_sales_team.groupby('Sales Rep Id')['Converted'].mean(),kde=False)  3. vals = ax.get_xticks() 4. ax.set_xticklabels(['{:,.0f}%'.format(x*100) for x in vals])  5. ax.set_title('Number of sales reps by conversion rate')  6. sns.despine()  

1. def vertical_mean_line(x, **kwargs):  2.     ls = {"0":"-","1":"--"}  3.     plt.axvline(x.mean(), linestyle =ls[kwargs.get("label","0")],   4.                 color = kwargs.get("color", "r"))  5.     txkw = dict(size=15, color = kwargs.get("color", "r"))  6.     tx = "mean: {:.1f}%\n(std: {:.1f}%)".format(x.mean()*100,x.std()*100)  7.     label_x_pos_adjustment = 0.015   8.     label_y_pos_adjustment = 20  9.     plt.text(x.mean() + label_x_pos_adjustment, label_y_pos_adjustment, tx, **txkw)  10.   11. sns.set(  12.     font_scale=1.5,  13.     style="whitegrid"  14. )  15.   16. _ = orders_with_sales_team.groupby('Sales Rep Id').agg({  17.     'Converted': np.mean,  18.     'Company Id': pd.Series.nunique  19. })  20. _.columns = ['conversion rate','number of accounts']  21.   22. g = sns.FacetGrid(_, col="number of accounts", height=4, aspect=0.9, col_wrap=5)  23. g.map(sns.kdeplot, "conversion rate", shade=True)  24. g.set(xlim=(0, 0.35))  25. g.map(vertical_mean_line, "conversion rate")

1. invoices['Date of Meal'] = pd.to_datetime(invoices['Date of Meal']) 2. invoices['Date of Meal'].dt.time.value_counts().sort_index()  

1. invoices['Type of Meal'] = pd.cut(  2.     invoices['Date of Meal'].dt.hour,  3.     bins=[0,10,15,24],  4.     labels=['breakfast','lunch','dinner']  5. )

1. def replace(x):  2.     return x.replace("\n ",",").replace("' '","','").replace("'",'"')  3.   4. invoices['Participants'] = invoices['Participants'].apply(lambda x: replace(x))  5. invoices['Number Participants'] = invoices['Participants'].apply(lambda x:  len(json.loads(x)))

1. # combine order_leads with invoice data  2. orders_with_invoices = pd.merge(order_leads,invoices,how='left',on='Company Id')  3.   4. # calculate days between order leads and invocies  5. orders_with_invoices['Days of meal before order'] = (  6.     pd.to_datetime(orders_with_invoices['Date']) - orders_with_invoices['Date of Meal']  7. ).dt.days  8.   


    
9. # limit to only meals that are within 5 days of the order  10. orders_with_invoices = orders_with_invoices[abs(orders_with_invoices['Days of meal before order']) < 5]  11.   12. # To mnake sure that we don't cross assign meals to multiple orders and therefore create duplicates  13. # we first sort our data by absolute distance to the orders  14. orders_with_invoices = orders_with_invoices.loc[  15.     abs(orders_with_invoices['Days of meal before order']).sort_values().index  16. ]  17.   18. # keep the first (i.e. closest to sales event) sales order  19. orders_with_invoices = orders_with_invoices.drop_duplicates(subset=['Order Id'])  20.   21. orders_without_invoices = order_leads[~order_leads['Order Id'].isin(orders_with_invoices['Order Id'].unique())]  22.   23. orders_with_meals = pd.concat([orders_with_invoices,orders_without_invoices],sort=True)

1. def plot_bars(data,x_col,y_col):  2.     data = data.reset_index()  3.     sns.set(  4.         font_scale=1.5,  5.         style="whitegrid",  6.         rc={'figure.figsize':(20,7)}  7.     )  8.     g = sns.barplot(x=x_col, y=y_col, data=data, color='royalblue')  9.   10.     for p in g.patches:  11.         g.annotate(  12.             format(p.get_height(), '.2%'),  13.             (p.get_x() + p.get_width() / 2., p.get_height()),   14.             ha = 'center',   15.             va = 'center',   16.             xytext = (0, 10),   17.             textcoords = 'offset points'  18.         )  19.           20.     vals = g.get_yticks()  21.     g.set_yticklabels(['{:,.0f}%'.format(x*100) for x in vals])  22.   23.     sns.despine()

1. orders_with_meals['Type of Meal'].fillna('no meal',inplace=True)  2. _ = orders_with_meals.groupby('Type of Meal').agg({'Converted': np.mean})  3. plot_bars(_,x_col='Type of Meal',y_col='Converted'


    
)

1. _ = orders_with_meals.groupby(['Days of meal before order']).agg(  2.     {'Converted': np.mean}  3. )  4. plot_bars(data=_,x_col='Days of meal before order',y_col='Converted'))

1. def draw_heatmap(data,inner_row, inner_col, outer_row, outer_col, values):  2.     sns.set(font_scale=1)  3.     fg = sns.FacetGrid(  4.         data,   5.         row=outer_row,  6.         col=outer_col,   7.         margin_titles=True  8.     )  9.   10.     position = left, bottom, width, height = 1.4, .2, .1, .6  11.     cbar_ax = fg.fig.add_axes(position)   12.   13.     fg.map_dataframe(  14.         draw_heatmap_facet,   15.         x_col=inner_col,  16.         y_col=inner_row,   17.         values=values,   18.         cbar_ax=cbar_ax,  19.         vmin=0,   20.         vmax=.4  21.     )  22.   23.     fg.fig.subplots_adjust(right=1.3)    24.     plt.show()  25.   26. def draw_heatmap_facet(*args, **kwargs):  27.     data = kwargs.pop('data')  28.     x_col = kwargs.pop('x_col')  29.     y_col = kwargs.pop('y_col')  30.     values = kwargs.pop('values')  31.     d = data.pivot(index=y_col, columns=x_col, values=values)  32.     annot = round(d,4).values  33.     cmap = sns.color_palette("RdYlGn",30)  34.     # cmap = sns.color_palette("PuBu",30) alternative color coding  35.     sns.heatmap(d, **kwargs, annot=annot, center=0, fmt=".1%", cmap=cmap, linewidth=.5)  

1. # Aggregate the data a bit  2. 


    
orders_with_meals['Meal Price / Order Value'] = orders_with_meals['Meal Price']/orders_with_meals['Order Value']  3. orders_with_meals['Meal Price / Order Value'] = pd.qcut(  4.     orders_with_meals['Meal Price / Order Value']*-1,  5.     5,  6.     labels = ['Least Expensive','Less Expensive','Proportional','More Expensive','Most Expensive'][::-1]  7. )  8.   9. orders_with_meals['Timing of Meal'] = pd.qcut(  10.     orders_with_meals['Days of meal before order'],  11.     3,  12.     labels = ['After Order','Around Order','Before Order']  13. )  14.   15.   16. data = orders_with_meals[orders_with_meals['Type of Meal'] != 'no meal'].groupby(  17.     ['Timing of Meal','Number Participants','Type of Meal','Meal Price / Order Value']  18. ).agg({'Converted': np.mean}).unstack().fillna(0).stack().reset_index()

1. draw_heatmap(  2.     data=data,   3.     outer_row='Timing of Meal',  4.     outer_col='Type of Meal',  5.     inner_row='Meal Price / Order Value',  6.     inner_col='Number Participants',  7.     values='Converted'  8. )

!pip install matplotlib==3.1.0