How to use pivot_table () to realize the data perspective function in Python

This article is to share with you about how to use pivot_table () in Python to achieve data perspective function, the editor thinks it is very practical, so I share it with you to learn. I hope you can get something after reading this article.

Pivot_table

The pivot () function does not have data aggregation function. To achieve this function, you need to call the third top-level function in the Pandas package: pivot_table (). The project location in pandas is as follows:

Pandas

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Pivot_table ()

Construct a df instance as follows:

Call the following actions:

The parameter index indicates that An and B are row indexes, columns indicates that the value of column C is the column, the aggregate function is the sum, and values is the D column after the two axes (index and columns) are determined. The results are as follows:

Among them, the aggregate function can be extended more richly, using more than one. As shown below, D and E are selected for the intersection of the two axes, np.mean () is used for aggregation in column D, and np.sum, np.mean, np.max, np.min for column E

The results are as follows:

Function prototype

Fill_value: the fill value of the null value

Dropna: if all elements in a column are np.nan, whether to discard them

Margins: summary column, margins_name: summary name

The margins parameter defaults to False. If set to True, you will get a summary of each column, as shown in the following df instance

Set margins to True and summary line index to name and customize it to self_name:

Be careful

When margins is set to True, pandas version 0.22.3 only supports aggregate functions as a single element, but not list, as shown below:

An exception will be reported:

Through the pivot_table aggregation function source code (shown below), we find that it itself is implemented by calling groupby () and its agg ().

Grouped = data.groupby (keys, observed=False) agged = grouped.agg (aggfunc) the above is how to use pivot_table () to achieve data perspective in Python. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.