39 Pandas处理Excel - 复杂多列到多行转换

39 Pandas处理Excel - 复杂多列到多行转换

用户需求图片

分析:

  1. 一行变多行,可以用explode实现;
  2. 要使用explode,需要先将多列变成一列;
  3. 注意有的列为空,需要做空值过滤;

1. 读取数据

import pandas as pd file_path = "./course_datas/c39_explode_to_manyrows/读者提供的数据-输入.xlsx" df = pd.read_excel(file_path) df
.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>
P/N Description Supplier Supplier PN Supplier.1 Supplier PN.1 Supplier.2 Supplier PN.2
0 302-462-326 CAP CER 0402 100pF 5% 50V MURATA GRM1555C1H101JA01D YAGEO CC0402JRNPO9BN101 GRM1555C1H101JA01J Murata Electronics North America
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V AVX Corporation 04025A6R8CAT2A KEMET C0402C689C5GACTU NaN NaN
2 302-462-009 CAP CER 0402 3.9pF 0.25pF 50V AVX Corporation 04025A3R9CAT2A NaN NaN NaN NaN

2. 把多列合并到一列

# 提取待合并的所有列名,一会可以把它们drop掉 merge_names = list(df.loc[:, "Supplier":].columns.values) merge_names ['Supplier', 'Supplier PN', 'Supplier.1', 'Supplier PN.1', 'Supplier.2', 'Supplier PN.2'] def merge_cols(x): """ x是一个行Series,把它们按分隔符合并 """ # 删除为空的列 x = x[x.notna()] # 使用x.values用于合并 y = x.values # 合并后的列表,每个元素是"Supplier" + "Supplier PN"对 result = [] # range的步长为2,目的是每两列做合并 for idx in range(0, len(y), 2): # 使用竖线作为"Supplier" + "Supplier PN"之间的分隔符 result.append(f"{y[idx]}|{y[idx+1]}") # 将所有两两对,用#分割,返回一个大字符串 return "#".join(result) # 添加新列,把待合并的所有列变成一个大字符串 df["merge"] = df.loc[:, "Supplier":].apply(merge_cols, axis=1) df
.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>
P/N Description Supplier Supplier PN Supplier.1 Supplier PN.1 Supplier.2 Supplier PN.2 merge
0 302-462-326 CAP CER 0402 100pF 5% 50V MURATA GRM1555C1H101JA01D YAGEO CC0402JRNPO9BN101 GRM1555C1H101JA01J Murata Electronics North America MURATA|GRM1555C1H101JA01D#YAGEO|CC0402JRNPO9BN...
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V AVX Corporation 04025A6R8CAT2A KEMET C0402C689C5GACTU NaN NaN AVX Corporation|04025A6R8CAT2A#KEMET|C0402C689...
2 302-462-009 CAP CER 0402 3.9pF 0.25pF 50V AVX Corporation 04025A3R9CAT2A NaN NaN NaN NaN AVX Corporation|04025A3R9CAT2A
# 把不用的列删除掉 df.drop(merge_names, axis=1, inplace=True) df
.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>
P/N Description merge
0 302-462-326 CAP CER 0402 100pF 5% 50V MURATA|GRM1555C1H101JA01D#YAGEO|CC0402JRNPO9BN...
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V AVX Corporation|04025A6R8CAT2A#KEMET|C0402C689...
2 302-462-009 CAP CER 0402 3.9pF 0.25pF 50V AVX Corporation|04025A3R9CAT2A

3. 使用explode把一列变多行

# 先将merge列变成list的形式 df["merge"] = df["merge"].str.split("#") df
.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>
P/N Description merge
0 302-462-326 CAP CER 0402 100pF 5% 50V [MURATA|GRM1555C1H101JA01D, YAGEO|CC0402JRNPO9...
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V [AVX Corporation|04025A6R8CAT2A, KEMET|C0402C6...
2 302-462-009 CAP CER 0402 3.9pF 0.25pF 50V [AVX Corporation|04025A3R9CAT2A]
# 执行explode变成多行 df_explode = df.explode("merge") df_explode
.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>
P/N Description merge
0 302-462-326 CAP CER 0402 100pF 5% 50V MURATA|GRM1555C1H101JA01D
0 302-462-326 CAP CER 0402 100pF 5% 50V YAGEO|CC0402JRNPO9BN101
0 302-462-326 CAP CER 0402 100pF 5% 50V GRM1555C1H101JA01J|Murata Electronics North Am...
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V AVX Corporation|04025A6R8CAT2A
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V KEMET|C0402C689C5GACTU
2 302-462-009 CAP CER 0402 3.9pF 0.25pF 50V AVX Corporation|04025A3R9CAT2A

4. 将一列还原成结果的多列

# 分别从merge中提取两列 df_explode["Supplier"]=df_explode["merge"].str.split("|").str[0] df_explode["Supplier PN"]=df_explode["merge"].str.split("|").str[1] df_explode
.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>
P/N Description merge Supplier Supplier PN
0 302-462-326 CAP CER 0402 100pF 5% 50V MURATA|GRM1555C1H101JA01D MURATA GRM1555C1H101JA01D
0 302-462-326 CAP CER 0402 100pF 5% 50V YAGEO|CC0402JRNPO9BN101 YAGEO CC0402JRNPO9BN101
0 302-462-326 CAP CER 0402 100pF 5% 50V GRM1555C1H101JA01J|Murata Electronics North Am... GRM1555C1H101JA01J Murata Electronics North America
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V AVX Corporation|04025A6R8CAT2A AVX Corporation 04025A6R8CAT2A
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V KEMET|C0402C689C5GACTU KEMET C0402C689C5GACTU
2 302-462-009 CAP CER 0402 3.9pF 0.25pF 50V AVX Corporation|04025A3R9CAT2A AVX Corporation 04025A3R9CAT2A
# 把merge列删除掉,得到最终数据 df_explode.drop("merge", axis=1, inplace=True) df_explode
.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>
P/N Description Supplier Supplier PN
0 302-462-326 CAP CER 0402 100pF 5% 50V MURATA GRM1555C1H101JA01D
0 302-462-326 CAP CER 0402 100pF 5% 50V YAGEO CC0402JRNPO9BN101
0 302-462-326 CAP CER 0402 100pF 5% 50V GRM1555C1H101JA01J Murata Electronics North America
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V AVX Corporation 04025A6R8CAT2A
1 302-462-012 CAP CER 0402 6.8pF 0.25pF 50V KEMET C0402C689C5GACTU
2 302-462-009 CAP CER 0402 3.9pF 0.25pF 50V AVX Corporation 04025A3R9CAT2A

5. 输出到结果Excel

df_explode.to_excel("./course_datas/c39_explode_to_manyrows/读者提供的数据-输出.xlsx", index=False)

本文使用 文章同步助手 同步

©著作权归作者所有,转载或内容合作请联系作者
平台声明:文章内容(如有图片或视频亦包括在内)由作者上传并发布,文章内容仅代表作者本人观点,简书系信息发布平台,仅提供信息存储服务。

推荐阅读更多精彩内容