Python 乘法数据帧行取决于此行中的值

我有这样一个数据帧：

df = pd.DataFrame({'col1': [69, 77, 88],
                   'col2': ['bar34', 'barf30', 'barfoo29'],
                   'col3': [4, 2, 5]})
print(df, '\n')

我需要根据“col3”中的值乘以行。期望输出：

    col1      col2  col3
0     69     bar34     4
1     69     bar34     4
2     69     bar34     4
3     69     bar34     4
4     77    barf30     2
5     77    barf30     2
6     88  barfoo29     5 
7     88  barfoo29     5 
8     88  barfoo29     5 
9     88  barfoo29     5 
10    88  barfoo29     5 

---

我只有一个解决方案，但我敢肯定它根本没有效率

import numpy as np


# Get columns list
cols = df.columns.to_list()

# Loop for each row to multiply
for index, row in df.iterrows():

   # Loop for each new row we get
   full_array = []
   for new_row in range(row['col3']):
      row_lst = [row[col_name] for col_name in cols]
      full_array.append(row_lst)
   numpy_data = np.array(full_array)

   # Drop used row
   df = df.drop([index])

   # Creating mini_df
   mini_df = pd.DataFrame(numpy_data, columns=columns)

   # Concat with main dataframe
   df = pd.concat([df, mini_df], ignore_index=True)
   df = df.reset_index(drop=True)

print(df)

那么，这里有一个解决方案

---

---

---

实际上，您不需要设置索引：

df.loc[df.index.repeat（df['col3']）]。重置索引（drop=True）

也可以，因为

索引。repeat

重复现有索引

import numpy as np


# Get columns list
cols = df.columns.to_list()

# Loop for each row to multiply
for index, row in df.iterrows():

   # Loop for each new row we get
   full_array = []
   for new_row in range(row['col3']):
      row_lst = [row[col_name] for col_name in cols]
      full_array.append(row_lst)
   numpy_data = np.array(full_array)

   # Drop used row
   df = df.drop([index])

   # Creating mini_df
   mini_df = pd.DataFrame(numpy_data, columns=columns)

   # Concat with main dataframe
   df = pd.concat([df, mini_df], ignore_index=True)
   df = df.reset_index(drop=True)

print(df)

    col1      col2  col3
0     69     bar34     4
1     69     bar34     4
2     69     bar34     4
3     69     bar34     4
4     77    barf30     2
5     77    barf30     2
6     88  barfoo29     5 
7     88  barfoo29     5 
8     88  barfoo29     5 
9     88  barfoo29     5 
10    88  barfoo29     5 

df = df.set_index(df.col3)

print(
    df.reindex(df.index.repeat(df.col3))
        .reset_index(drop=True)
)

# suggested by @anky,

df.loc[df.index.repeat(df.col3)]

    col1      col2  col3
0     69     bar34     4
1     69     bar34     4
2     69     bar34     4
3     69     bar34     4
4     77    barf30     2
5     77    barf30     2
6     88  barfoo29     5
7     88  barfoo29     5
8     88  barfoo29     5
9     88  barfoo29     5
10    88  barfoo29     5