如何在數據框中添加列？

Question

我有以下代碼：

db_fields = ("id", "email", "status", "source")
df = DataFrame(results)
for col in db_fields:
    if col not in df.columns:
          COLUMN IS MISSING - COMMAND TO ADD COLUMN

例如，如果缺少status列，則需要將其添加到數據框中，而無需添加任何值。因此，當我將df導出到csv我將始終具有相同的字段架構。

我知道要刪除列，我應該這樣做：

df = df.drop(col, 1)

但是我不知道添加具有空值的列的最佳方法是什么。

Answer 1

此方法將在狀態列中添加Null值：

import numpy as np
df['status'] = np.nan

或者：

df['status'] = None

所以：

db_fields = ("id", "email", "status", "source")
for col in db_fields:
    if col not in df.columns:
        df[col] = None

Answer 2

您可以創建不存在的列的數組，並使用assign和dictionary創建新的列：

df = pd.DataFrame({'id': ['a1','a2', 'b1'],
                  'a': ['a1','a2', 'b1'],
                  'source': ['a1','a2', 'b1']})
print (df)
   id   a source
0  a1  a1     a1
1  a2  a2     a2
2  b1  b1     b1

db_fields = ("id", "email", "status", "source")

#get missing columns
diff = np.setdiff1d(np.array(db_fields), df.columns)
print (diff)
['email' 'status']

#get original columns not existed in db_fields
diff1 = np.setdiff1d(df.columns, np.array(db_fields)).tolist()
print (diff1)
['a']

#add missing columns with change order
d = dict.fromkeys(diff, np.nan)
df = df.assign(**d)[diff1 + list(db_fields)]
print (df)
    a  id  email  status source
0  a1  a1    NaN     NaN     a1
1  a2  a2    NaN     NaN     a2
2  b1  b1    NaN     NaN     b1

---

#if necessary first db_fields
df = df.assign(**d)[list(db_fields) + diff1]
print (df)
   id  email  status source   a
0  a1    NaN     NaN     a1  a1
1  a2    NaN     NaN     a2  a2
2  b1    NaN     NaN     b1  b1

Answer 3

在這里，只需一行就可以簡單明了地看到它：

import numpy as np
db_fields = ("id", "email", "status", "source")
df = DataFrame(results)
for col in db_fields:
    if col not in df.columns:
        # Add the column
        df[col] = np.nan

順便說一句：您也可以使用df.drop(inplace=True)刪除列。