Pandas:从系列创建数据帧 [英] Pandas: Creating DataFrame from Series

问题描述

我当前的代码如下所示 - 我正在导入一个 MAT 文件并尝试从其中的变量创建一个 DataFrame:

My current code is shown below - I'm importing a MAT file and trying to create a DataFrame from variables within it:

mat = loadmat(file_path)  # load mat-file
Variables = mat.keys()    # identify variable names

df = pd.DataFrame         # Initialise DataFrame

for name in Variables:

    B = mat[name]
    s = pd.Series (B[:,1])

因此在循环中我可以创建一系列每个变量(它们是具有两列的数组 - 所以我需要的值在第 2 列中)

So within the loop I can create a series of each variable (they're arrays with two columns - so the values I need are in column 2)

我的问题是如何将系列附加到数据框?我已经浏览了文档，但似乎没有一个示例适合我正在尝试做的事情.

My question is how do I append the series to the dataframe? I've looked through the documentation and none of the examples seem to fit what I'm trying to do.

此致，

本

推荐答案

这里是如何创建一个DataFrame，其中每个系列都是一行.

Here is how to create a DataFrame where each series is a row.

对于单个系列(导致单行数据帧):

For a single Series (resulting in a single-row DataFrame):

series = pd.Series([1,2], index=['a','b'])
df = pd.DataFrame([series])

对于具有相同索引的多个系列:

For multiple series with identical indices:

cols = ['a','b']
list_of_series = [pd.Series([1,2],index=cols), pd.Series([3,4],index=cols)]
df = pd.DataFrame(list_of_series, columns=cols)

对于可能具有不同索引的多个系列:

For multiple series with possibly different indices:

list_of_series = [pd.Series([1,2],index=['a','b']), pd.Series([3,4],index=['a','c'])]
df = pd.concat(list_of_series, axis=1).transpose()

要创建一个每个系列都是一列的 DataFrame，请参阅其他人的答案.或者，可以创建一个 DataFrame，其中每个系列都是一行，如上所述，然后使用 df.transpose().但是，如果列具有不同的数据类型，则后一种方法效率低下.

To create a DataFrame where each series is a column, see the answers by others. Alternatively, one can create a DataFrame where each series is a row, as above, and then use df.transpose(). However, the latter approach is inefficient if the columns have different data types.

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