pandas将list数据拆分成行或列

数据

import numpy as np
import pandas as pd

data = [{'Name': '小明', 'Chinese': [70, 80], 'Math': [90, 80]},
        {'Name': '小红', 'Chinese': [70, 80, 90], 'Math': [90, 80, 70]}]
data = pd.DataFrame(data)
data

拆分成行

def split_row(data, column):
    '''拆分成行

    :param data: 原始数据
    :param column: 拆分的列名
    :type data: pandas.core.frame.DataFrame
    :type column: str
    '''
    row_len = list(map(len, data[column].values))
    rows = []
    for i in data.columns:
        if i == column:
            row = np.concatenate(data[i].values)
        else:
            row = np.repeat(data[i].values, row_len)
        rows.append(row)
    return pd.DataFrame(np.dstack(tuple(rows))[0], columns=data.columns)


split_row(data, column='Chinese')

拆分成列

from copy import deepcopy


def split_col(data, column):
    '''拆分成列

    :param data: 原始数据
    :param column: 拆分的列名
    :type data: pandas.core.frame.DataFrame
    :type column: str
    '''
    data = deepcopy(data)
    max_len = max(list(map(len, data[column].values)))  # 最大长度
    new_col = data[column].apply(lambda x: x + [None]*(max_len - len(x)))  # 补空值，None可换成np.nan
    new_col = np.array(new_col.tolist()).T  # 转置
    for i, j in enumerate(new_col):
        data[column + str(i)] = j
    return data


split_col(data, column='Chinese')

其他情况

1. 批量处理+不要原列

def split_col(data, columns):
    '''拆分成列

    :param data: 原始数据
    :param columns: 拆分的列名
    :type data: pandas.core.frame.DataFrame
    :type columns: list
    '''
    for c in columns:
        new_col = data.pop(c)
        max_len = max(list(map(len, new_col.values)))  # 最大长度
        new_col = new_col.apply(lambda x: x + [None]*(max_len - len(x)))  # 补空值，None可换成np.nan
        new_col = np.array(new_col.tolist()).T  # 转置
        for i, j in enumerate(new_col):
            data[c + str(i)] = j


split_col(data, columns=['Chinese','Math'])
data

2. 带int和list数据

转成这样：

import numpy as np
import pandas as pd

data = [{'Name': '小爱', 'Chinese': 70, 'Math': 90},
        {'Name': '小明', 'Chinese': [70, 80], 'Math': [90, 80]},
        {'Name': '小红', 'Chinese': [70, 80, 90], 'Math': [90, 80, 70]}]
data = pd.DataFrame(data)

def split_col(data, columns):
    '''拆分成列

    :param data: 原始数据
    :param columns: 拆分的列名
    :type data: pandas.core.frame.DataFrame
    :type columns: list
    '''
    for c in columns:
        new_col = data.pop(c)
        max_len = max(list(map(lambda x:len(x) if isinstance(x, list) else 1, new_col.values)))  # 最大长度
        new_col = new_col.apply(lambda x: x+[None]*(max_len - len(x)) if isinstance(x, list) else [x]+[None]*(max_len - 1))  # 补空值，None可换成np.nan
        new_col = np.array(new_col.tolist()).T  # 转置
        for i, j in enumerate(new_col):
            data[c + str(i)] = j


split_col(data, columns=['Chinese','Math'])
data

参考文献

1. Python Pandas list(列表)数据列拆分成多行的方法
2. 10分钟了解Pandas基础知识