Python：從 DF 中特定部分的行到列

Question

我在excel中有下表，如下所示：

import pandas as pd

data = """  Col1  |  Col2  |  Col3  | Col4 
 Value11 | Value21 | Value31 |      
 stuff  | stuff  | stuff  |  2.0 
 stuff  | stuff  | stuff  |  3.0 
        |        | Total  |  5.0 
 Value12 | Value22 | Value32 |      
 stuff  | stuff  | stuff  |  6.0 
 stuff  | stuff  | stuff  |  4.0 
        |        | Total  | 10.0 """

df = pd.read_csv(pd.compat.StringIO(data), header=0, delimiter = "|")

print(df)

+---------+---------+---------+------+
|  Col1   |  Col2   |  Col3   | Col4 |
+---------+---------+---------+------+
| Value11 | Value21 | Value31 |      |
| stuff   | stuff   | stuff   |  2.0 |
| stuff   | stuff   | stuff   |  3.0 |
|         |         | Total   |  5.0 |
| Value12 | Value22 | Value32 |      |
| stuff   | stuff   | stuff   |  6.0 |
| stuff   | stuff   | stuff   |  4.0 |
|         |         | Total   | 10.0 |
+---------+---------+---------+------+

並且想要這樣，以便我可以進行數據分析：

+-------+-------+--------+------+----------+---------+---------+
| Col1  | Col2  |  Col3  | Col4 |   Col5   |  Col6   |  Col7   |
+-------+-------+--------+------+----------+---------+---------+
| stuff | stuff | stuff  |  2.0 | Value11  | Value21 | Value31 |
| stuff | stuff | stuff  |  3.0 | Value11  | Value21 | Value31 |
| stuff | stuff | stuff  |  6.0 | Value12  | Value22 | Value32 |
| stuff | stuff | stuff  |  4.0 | Value12  | Value22 | Value32 |
+-------+-------+--------+------+----------+---------+---------+

也就是說，我想將每個 Col1、Col2、Col3 中的值轉換為相應部分旁邊的重復行。

我看到的唯一模式是 Col3 中有一個“Total”變量，就在我想轉換為行的值的正上方。

關於如何在 Python 中實現這一點的任何想法？

Answer 1

你在尋找這樣的東西嗎？

import pandas as pd
df = pd.DataFrame(
    {"Col1": ["Value11", "stuff1", "stuff1","Value12", "stuff2", "stuff2"],
     "Col2": ["Value21", "stuff1", "stuff1","Value22", "stuff2", "stuff2"],
     "Col3": ["Value31", "stuff1", "stuff1","Value32", "stuff2", "stuff2"],
     "Col4": ["", 2, 3,"",6,4], },
    index=[1, 2, 3,4,5,6])

df1 = df.loc[df['Col1'] == 'stuff1']
df2 = df.loc[df['Col1'] == 'stuff2']

dfc = pd.concat([df1,df2])

df11 = df.loc[df['Col1'] == 'Value11']
df22 = df.loc[df['Col1'] == 'Value12']


dfc1 = pd.concat([df11, df11])
dfc2 = pd.concat([df22, df22])



df_fin1 = pd.concat([dfc1, dfc2])

print(df_fin1)
dfc.reset_index(drop=True, inplace=True)
df_fin1.reset_index(drop=True, inplace=True)
df_fin = pd.concat([dfc, df_fin1], axis=1)


print(df_fin)

Answer 2

因此，如果您只選擇行並對其進行操作，則代碼如下所示：

df21 = pd.concat([df.iloc[0:1], df.iloc[0:1]])
df22 = pd.concat([df.iloc[4:5], df.iloc[4:5]])

df2 = pd.concat([df21,df22])

df1 = pd.concat([df.iloc[1:3], df.iloc[5:7]])

df1.reset_index(drop=True, inplace=True)
df2.reset_index(drop=True, inplace=True)
df_f = pd.concat([df1, df2], axis=1)