pandas 交叉表矩阵点nansum [英] Pandas crosstab matrix dot nansum

问题描述

我正在寻找使用类似np.nansum的函数从现有数据帧创建子数据帧的帮助.我想将此表转换为非空列总和的矩阵:

i'm looking for help creating a sub-dataframe from an existing dataframe using a np.nansum-like function. I want to convert this table into a matrix of non-null column sums:

    dan ste bob
t1  na  2   na
t2  2   na  1
t3  2   1   na
t4  1   na  2
t5  na  1   2
t6  2   1   na
t7  1   na  2

例如，当"dan"不为null(t-2,3,4,6,7)时，"ste"的总和为2，而"bob"为5. "dan"的总和为4.

For example, when 'dan' is not-null (t-2,3,4,6,7) the sum of 'ste' is 2 and 'bob' is 5. When 'ste' is not-null the sum of 'dan' is 4.

    dan ste bob
dan 0   2   5
ste 4   0   2
bob 4   1   0

有什么想法吗?

提前谢谢！

我最终在下面使用了matt函数的修改版本:

I ended up using a modified version of matt's function below:

def nansum_matrix_create(df):
    rows = []
    for col in list(df.columns.values):

        col_sums = df[df[col] != 0].sum()
        rows.append(col_sums)

    return pd.DataFrame(rows, columns=df.columns, index=df.columns)

推荐答案

假定您的数据框没有大量的列，则此函数应该可以实现您想要的并且性能相当.我已经在各列之间使用for循环实现了此功能，因此可能会有更高性能/更优雅的解决方案.

Assuming your dataframe doesn't have large number of columns, this function should do what you want and be fairly performant. I have implemented this using for loop across columns so there may be a more performant / elegant solution out there.

import pandas as pd

# Initialise dataframe
df = {"dan":[pd.np.nan,2,2,1,pd.np.nan,2,1],
      "ste":[2,pd.np.nan,1,pd.np.nan,1,1,pd.np.nan],
      "bob":[pd.np.nan,1,pd.np.nan,2,2,pd.np.nan,2]}
df = pd.DataFrame(df)[["dan","ste","bob"]]

def matrix_create(df):
    rows = []
    for col in df.columns:
        subvals, index = [], []
        for subcol in df.columns:
            index.append(subcol)
            if subcol == col:
                subvals.append(0)
            else:
                subvals.append(df[~pd.isnull(df[col])][subcol].sum())

        rows.append(subvals)

    return pd.DataFrame(rows,columns=df.columns,index=index)

matrix_create(df)

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