In python, pandas. DataFrame sums rows and columns and adds the new row and column sample code.

Pandas is the most famous data statistics package in the python environment, while DataFrame is translated as a data frame, which is a data organization method. This article mainly introduces pandas in python. dataFrame sums rows and columns and adds new rows and columns. the detailed sample code is provided in this article. For more information, see the following. Pandas is the most famous data statistics package in the python environment, while DataFrame is translated as a data frame, which is a data organization method. This article mainly introduces pandas in python. dataFrame sums rows and columns and adds sample code for new rows and columns. the detailed sample code is provided in this article. For more information, see the following.

This article introduces pandas. DataFrame in python to sum rows and columns and add new rows and columns. I will not talk about it below. let's take a look at the details.

The method is as follows:

Import module:

from pandas import DataFrameimport pandas as pdimport numpy as np

Generate DataFrame data

df = DataFrame(np.random.randn(4, 5), columns=['A', 'B', 'C', 'D', 'E'])

DataFrame data preview:

  A  B  C  D  E0 0.673092 0.230338 -0.171681 0.312303 -0.1848131 -0.504482 -0.344286 -0.050845 -0.811277 -0.2981812 0.542788 0.207708 0.651379 -0.656214 0.5075953 -0.249410 0.131549 -2.198480 -0.437407 1.628228

Calculate the total data of each column and add it to the end as a new column

df['Col_sum'] = df.apply(lambda x: x.sum(), axis=1)

Calculates the total data of each row and adds it to the end as a new row.

df.loc['Row_sum'] = df.apply(lambda x: x.sum())

Final data result:

  A  B  C  D  E Col_sum0 0.673092 0.230338 -0.171681 0.312303 -0.184813 0.8592381 -0.504482 -0.344286 -0.050845 -0.811277 -0.298181 -2.0090712 0.542788 0.207708 0.651379 -0.656214 0.507595 1.2532563 -0.249410 0.131549 -2.198480 -0.437407 1.628228 -1.125520Row_sum 0.461987 0.225310 -1.769627 -1.592595 1.652828 -1.022097

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