在Pandas中创建DataFrame时填充默认0 [英] Fill with default 0's when creating a DataFrame in Pandas

问题描述

我有一个输入到列表的字符串字典，列表的长度可能不同.

I have an input dict-of-string-to-list with possibly different lengths for the list.

d = {'b': [2,3], 'a': [1]}

当我这样做时:df = pd.DataFrame(data=d)， 我看到 ValueError:数组的长度必须相同

when I do: df = pd.DataFrame(data=d), i'm seeing ValueError: arrays must all be same length

问题:创建df时如何用默认值(例如0)填充缺失值?

Question: How do i fill the missing values with default (e.g. 0) when creating the df?

创建df的原因是获得以下最终结果: {'b': 3}

The reason to create the df is to get the final result of: {'b': 3}

3是列表中所有数字的最大值.

whereas 3 is the max of all numbers in the lists.

推荐答案

您可以使用 .fillna 并转置以将键设置为列:

You can use DataFrame.from_dict setting orient to index so the keys of the dictionary are used as indices and the missing values are set to NaN. Then simply fill NaNs using .fillna and transpose to set the keys as columns:

pd.DataFrame.from_dict(d, orient='index').fillna(0).T

    b    a
0  2.0  1.0
1  3.0  0.0

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