python - 简单: df. A = sr中隐藏的地雷

Question

像df.A = sr这样的简单操作(将pandas.Series分配给pandas.DataFrame中的列)似乎无害，但它有许多细微差别。对于我这样刚开始学习pandas的人来说，它带来了很多便利，同时也带来了困惑。

下面给出一个简单的示例/挑战:

df:
+----+-----+
|    |   A |
|----+-----|
|  0 |   0 |
|  1 |   0 |
|  2 |   0 |
|  3 |   0 |
|  4 |   0 |
+----+-----+

l = [777, 666, 555, 444, 333]

sr:
+----+-----+
|    |   0 |
|----+-----|
|  7 | 777 |
|  6 | 666 |
|  5 | 555 |
|  4 | 444 |
|  3 | 333 |
+----+-----+

df.A = sr 之后的 df 是什么样子？
或
df.A = l 之后的 df 是什么样的？

根据我目前的理解，我分解了df.A = sr中的所有隐含操作，请纠正/确认/扩展: 例如，我不完全确定正确的术语。

# [0] a column in a DataFrame, is a Series, is a dictionary of index and values
# all cell to cell transfers are key-lookup based, individual element in an 
# index is called a "label" for a reason.

# [1] if sr didn't have some of the index labels in df.col's index, 
# the old values in those cells in df.col gets WIPED!
df.loc[ ~df.index.isin(sr.index)] = np.nan

# [2] values are transferred from sr cells into df cells with common index-labels. 
# As expected
df.loc[ df.index.isin(sr.index), 'A'] = 
    sr.loc[ [idx for idx in sr.index if idx in df.index] ]

# [3] sr's cells, whoes index-lables are not found in df.index, are ignored and 
# doesn't get to be assigned in df
sr.loc[ ~sr.index.isin(df.index)] # goes no where.

# [4] with all the wipping and ignore from above steps, 
# there is no error message or warnings.
# it can cause your mistakes to slip thru:
"""
df = pd.DataFrame(0, columns=['A'], index=np.arange(5))
df.loc[ df.index.isin( ['A', 'B']), 'A'] = sr
print(df)

df = pd.DataFrame(0, columns=['A'], index=[])
df.A = sr
print(df)
"""

剧透。设置和结果:

df = pd.DataFrame(0, columns=['A'], index=np.arange(5))
l = [777, 666, 555, 444, 333]
sr = pd.Series(l, index=[7, 6, 5, 4, 3])


RESULTS:
df.A = sr
df:
+----+-----+
|    |   A |
|----+-----|
|  0 | nan |
|  1 | nan |
|  2 | nan |
|  3 | 333 |
|  4 | 444 |
+----+-----+

df.A = l
df:
+----+-----+
|    |   A |
|----+-----|
|  0 | 777 |
|  1 | 666 |
|  2 | 555 |
|  3 | 444 |
|  4 | 333 |
+----+-----+

Answer 1

所以您看到的结果是由于以下原因:

sr = pd.Series(l, index=[7, 6, 5, 4, 3])

您将 l 的索引值专门分配给 [7, 6, 5, 4, 3]。

当你这样做时:

df.A = sr

该系列保持其指数值。然后当你定义 df 时:

df = pd.DataFrame(0, columns=['A'], index=np.arange(5))

您确保最高索引值为 4 (index=np.arange(5))

因此，您的列输出保留了 sr 的索引值，并将这些值放入 A 中，因此仅显示索引 3,4 值。

当你这样做时:

df.A = l

您只需将 l 中的值分配给 A 列。因此所有值都会出现。如果您将 sr = pd.Series(l, index=[7, 6, 5, 4, 3]) 更改为 sr = pd.Series(l)，则设置 df.A = sr 。您最终会得到与 df.A = l 完全相同的结果。