使用不同的列类型和值清理 Pandas df答案

【问题标题】：Cleaning Pandas df with varying column types and values使用不同的列类型和值清理 Pandas df
【发布时间】：2022-07-28 00:09:12
【问题描述】：

晚上好，

我的 pandas df (python) 看起来像这样：

我想做以下事情：

使用 col 0 和 col 1 创建日期列 -> 4 月 11 日
加入日期和第一个数值之间的字符串，然后将其标记为描述 1。
提取第一个数值并将其标记为 Amount 1
提取第二个数值并将其标记为 Amount 2
加入数值后面的字符串并将其标记为描述 2。

最后，我的 pandas df 会：

日期：4月11日
说明 1：abcd efgh ijklmnop
金额 1：425.85（12.34 只是图中的占位符金额）
金额 2：365.12（12.34 只是图中的占位符金额）
说明 2：ab cdefgh ijklm

我如何有效地清理这个 df 以达到我想要的结果？

谢谢！

样本数据：

{0: {20: '11', 21: '11', 22: '14', 23: '16', 24: '18', 25: '19', 26: '19'}, 1: {20: 'Apr', 21: 'Apr', 22: 'Apr', 23: 'Apr', 24: 'Apr', 25: 'Apr', 26: 'Apr'}, 2: {20: 'ACTNOWQUICK', 21: 'Cash', 22: 'ACTNOWQUICK', 23: 'ACTNOWQUICK', 24: 'Inward', 25: 'Cash', 26: 'Inward'}, 3: {20: '1234.56', 21: 'WithdrawalATM', 22: '76.53', 23: '1236.00', 24: 'DR', 25: 'WithdrawalATM', 26: 'CR'}, 4: {20: '1234.98', 21: '50.00', 22: '653.24', 23: '1234.78', 24: 'FUTHN', 25: '70.00', 26: 'YJHK'}, 5: {20: 'HYE912630964589376', 21: '1111.22', 22: 'HYE91234234589376', 23: 'HYE91263234234234376', 24: '60.00', 25: '222.22', 26: '33333.33'}, 6: {20: 'PLUTO', 21: '23523455', 22: 'WiN', 23: 'YOU', 24: '11.11', 25: '123123123', 26: '18.18'}, 7: {20: 'THEATRE', 21: None, 22: 'OTHR', 23: 'TECHY', 24: 'WOL', 25: None, 26: 'OTHER'}, 8: {20: 'OTHER', 21: None, 22: 'JOHNKLING', 23: 'BRO', 24: 'E54E236A58', 25: None, 26: 'Other'}, 9: {20: 'WUN', 21: None, 22: None, 23: 'OTHER', 24: 'FFF', 25: None, 26: 'PFFS'}, 10: {20: 'Cool', 21: None, 22: None, 23: '123123123523452', 24: 'UEJH', 25: None, 26: '(JUPITER)'}, 11: {20: 'Beans', 21: None, 22: None, 23: None, 24: None, 25: None, 26: 'EVEREST'}, 12: {20: 'KIng', 21: None, 22: None, 23: None, 24: None, 25: None, 26: '236272345235'}, 13: {20: None, 21: None, 22: None, 23: None, 24: None, 25: None, 26: None}, 14: {20: None, 21: None, 22: None, 23: None, 24: None, 25: None, 26: None}, 15: {20: None, 21: None, 22: None, 23: None, 24: None, 25: None, 26: None}, 16: {20: None, 21: None, 22: None, 23: None, 24: None, 25: None, 26: None}}

【问题讨论】：

请将数据共享为字典
已编辑以提供示例数据。谢谢。
所以你可以在一行中有多个描述，对吧？而且描述的数量是可变的吗？
没错。所以我想将它们合并到 2 个字段中。描述1和描述2。描述1将取日期到第一个数值之间的所有字符串（带小数的数字），描述2将取第二个数值之后的所有字符串（带小数的数字）
那么到底会有两个数值吗？或您拥有的数值的数量，您想创建那么多描述列。

标签： python pandas

【解决方案1】：

我们可以加入not None 值上的列，然后可以使用pd.extract 和expand = True 选项应用正则表达式来获取所需的组。

dict_ = {0: {20: '11', 21: '11', 22: '14', 23: '16', 24: '18', 25: '19', 26: '19'}, 1: {20: 'Apr', 21: 'Apr', 22: 'Apr', 23: 'Apr', 24: 'Apr', 25: 'Apr', 26: 'Apr'}, 2: {20: 'ACTNOWQUICK', 21: 'Cash', 22: 'ACTNOWQUICK', 23: 'ACTNOWQUICK', 24: 'Inward', 25: 'Cash', 26: 'Inward'}, 3: {20: '1234.56', 21: 'WithdrawalATM', 22: '76.53', 23: '1236.00', 24: 'DR', 25: 'WithdrawalATM', 26: 'CR'}, 4: {20: '1234.98', 21: '50.00', 22: '653.24', 23: '1234.78', 24: 'FUTHN', 25: '70.00', 26: 'YJHK'}, 5: {20: 'HYE912630964589376', 21: '1111.22', 22: 'HYE91234234589376', 23: 'HYE91263234234234376', 24: '60.00', 25: '222.22', 26: '33333.33'}, 6: {20: 'PLUTO', 21: '23523455', 22: 'WiN', 23: 'YOU', 24: '11.11', 25: '123123123', 26: '18.18'}, 7: {20: 'THEATRE', 21: None, 22: 'OTHR', 23: 'TECHY', 24: 'WOL', 25: None, 26: 'OTHER'}, 8: {20: 'OTHER', 21: None, 22: 'JOHNKLING', 23: 'BRO', 24: 'E54E236A58', 25: None, 26: 'Other'}, 9: {20: 'WUN', 21: None, 22: None, 23: 'OTHER', 24: 'FFF', 25: None, 26: 'PFFS'}, 10: {20: 'Cool', 21: None, 22: None, 23: '123123123523452', 24: 'UEJH', 25: None, 26: '(JUPITER)'}, 11: {20: 'Beans', 21: None, 22: None, 23: None, 24: None, 25: None, 26: 'EVEREST'}, 12: {20: 'KIng', 21: None, 22: None, 23: None, 24: None, 25: None, 26: '236272345235'}, 13: {20: None, 21: None, 22: None, 23: None, 24: None, 25: None, 26: None}, 14: {20: None, 21: None, 22: None, 23: None, 24: None, 25: None, 26: None}, 15: {20: None, 21: None, 22: None, 23: None, 24: None, 25: None, 26: None}, 16: {20: None, 21: None, 22: None, 23: None, 24: None, 25: None, 26: None}}
df = pd.DataFrame(dict_)
df[['Decription1', 'Amount1', 'Amount2', 'Description2']] = df[df.columns[~df.columns.isin([0,1])]].apply(lambda x: ' '.join(x.dropna()), axis=1).str.extract(r'([a-zA-Z ]*)([0-9]*[,.][0-9]*).*([0-9]*[,.][0-9]*)(.*)', expand=True)

输出

这给了我们预期的输出

【讨论】：