How to implement PivotTable with Python
This article mainly shows you "Python how to achieve PivotTable", the content is easy to understand, clear, hope to help you solve your doubts, the following let the editor lead you to study and learn "how to achieve PivotTable in Python" this article.
It can be achieved with Pandas in Python, although it feels that Excel is more convenient
1.groupby + agg
It's not intuitive enough, it's not pretty.
Create a data perspective on the year and type of loan
Train_data.groupby (['year_of_loan',' class']) .agg (d_roat = ('isDefault',' mean'))
2. Crosstabpandas.crosstab (index, columns,values, rownames=None, colnames, aggfunc, margins, margins_name, dropna, normalize)
The main parameters used are:
Index: which variable is selected as the PivotTable row
Columns: which variable is selected as the column of the PivotTable report
Values: the value to be aggregated
Aggfunc: aggregate function used
Margins: whether to add summary columns / rows
Margins_name: name of the summary row / column
Examples
Create a data perspective on the year and type of loan
Pd.crosstab (train_data ['year_of_loan'], train_data [' class'], train_data ['loan_id'], aggfunc='count',margins = True, margins_name =' Total')
You can directly see the proportion of default after cross-combination.
Pd.crosstab (train_data ['year_of_loan'], train_data [' class'], train_data ['isDefault'], aggfunc='mean')
3.groupby + pivottrain_data.groupby (['year_of_loan',' class'], as_index = False) ['isDefault'] .mean () .pivot (' year_of_loan', 'class',' isDefault')
Pivot_tablepandas.pivot_table (data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)
The common parameters are the same as crosstab.
Examples
Implement the same PivotTable report
Pandas.pivot_table (data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)
Pd.pivot_table (train_data [['year_of_loan',' class', 'isDefault']], values='isDefault', index= [' year_of_loan'], columns= ['class'], aggfunc='mean')
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