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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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