我提出了一个具有任意列数的数据框架的解决方案(同时一次仍然只分离一列的条目)。

def splitDataFrameList(df,target_column,separator):
    ''' df = dataframe to split,
    target_column = the column containing the values to split
    separator = the symbol used to perform the split

    returns: a dataframe with each entry for the target column separated, with each element moved into a new row. 
    The values in the other columns are duplicated across the newly divided rows.
    '''
    def splitListToRows(row,row_accumulator,target_column,separator):
        split_row = row[target_column].split(separator)
        for s in split_row:
            new_row = row.to_dict()
            new_row[target_column] = s
            row_accumulator.append(new_row)
    new_rows = []
    df.apply(splitListToRows,axis=1,args = (new_rows,target_column,separator))
    new_df = pandas.DataFrame(new_rows)
    return new_df

一种使用.apply()改变列格式的简单方法，以便.explod()可以使用它:

import string
import pandas as pd
from io import StringIO

file = StringIO("""    var1  var2
0  a,b,c     1
1  d,e,f     2""")

df = pd.read_csv(file, sep=r'\s\s+')

df['var1'] = df['var1'].apply(lambda x : str(x).split(','))

df.explode('var1')

输出:

  var1  var2
0   a   1
0   b   1
0   c   1
1   d   2
1   e   2
1   f   2

这是我为这个常见任务写的函数。它比Series/stack方法更有效。列的顺序和名称被保留。

def tidy_split(df, column, sep='|', keep=False):
    """
    Split the values of a column and expand so the new DataFrame has one split
    value per row. Filters rows where the column is missing.

    Params
    ------
    df : pandas.DataFrame
        dataframe with the column to split and expand
    column : str
        the column to split and expand
    sep : str
        the string used to split the column's values
    keep : bool
        whether to retain the presplit value as it's own row

    Returns
    -------
    pandas.DataFrame
        Returns a dataframe with the same columns as `df`.
    """
    indexes = list()
    new_values = list()
    df = df.dropna(subset=[column])
    for i, presplit in enumerate(df[column].astype(str)):
        values = presplit.split(sep)
        if keep and len(values) > 1:
            indexes.append(i)
            new_values.append(presplit)
        for value in values:
            indexes.append(i)
            new_values.append(value)
    new_df = df.iloc[indexes, :].copy()
    new_df[column] = new_values
    return new_df

有了这个函数，原来的问题就像这样简单:

tidy_split(a, 'var1', sep=',')

对于这个问题，我提出了以下解决方案:

def iter_var1(d):
    for _, row in d.iterrows():
        for v in row["var1"].split(","):
            yield (v, row["var2"])

new_a = DataFrame.from_records([i for i in iter_var1(a)],
        columns=["var1", "var2"])

我的版本的解决方案添加到这个集合!: -)

# Original problem
from pandas import DataFrame
import numpy as np
a = DataFrame([{'var1': 'a,b,c', 'var2': 1},
               {'var1': 'd,e,f', 'var2': 2}])
b = DataFrame([{'var1': 'a', 'var2': 1},
               {'var1': 'b', 'var2': 1},
               {'var1': 'c', 'var2': 1},
               {'var1': 'd', 'var2': 2},
               {'var1': 'e', 'var2': 2},
               {'var1': 'f', 'var2': 2}])
### My solution
import pandas as pd
import functools
def expand_on_cols(df, fuse_cols, delim=","):
    def expand_on_col(df, fuse_col):
        col_order = df.columns
        df_expanded = pd.DataFrame(
            df.set_index([x for x in df.columns if x != fuse_col])[fuse_col]
            .apply(lambda x: x.split(delim))
            .explode()
        ).reset_index()
        return df_expanded[col_order]
    all_expanded = functools.reduce(expand_on_col, fuse_cols, df)
    return all_expanded

assert(b.equals(expand_on_cols(a, ["var1"], delim=",")))

这里有很多答案，但我很惊讶没有人提到内置的熊猫爆炸功能。看看下面的链接: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.explode.html#pandas.DataFrame.explode

由于某种原因，我无法访问该函数，所以我使用下面的代码:

import pandas_explode
pandas_explode.patch()
df_zlp_people_cnt3 = df_zlp_people_cnt2.explode('people')

以上是我的数据样本。如你所见，人物栏有一系列人物，我试图把它炸开。我给出的代码适用于列表类型数据。因此，请尝试将逗号分隔的文本数据转换为列表格式。此外，由于我的代码使用内置函数，它比自定义/应用函数快得多。

注意:你可能需要用pip安装pandas_explosion。