pandas 用数组替换熊猫列值
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Replace pandas column values with array
提问by Adam Schroeder
I have an array:
我有一个数组：
([ 137.55021238, 125.30017675, 130.20181675, 109.47348838])
I need the array values to replace the b column, with the index number remaining the same:
我需要数组值来替换 b 列，索引号保持不变：
Index a b
0 0.671399 Nan
35 0.446172 Nan
63 0.614758 Nan
72 0.634448 Nan
I tried to use replace but it didn't work. Is there another way of doing this without turning array into a dataframe and merging?
我尝试使用替换，但没有用。是否有另一种方法可以在不将数组转换为数据帧并合并的情况下执行此操作？
回答by cs95
vals = [137.55021238, 125.30017675, 130.20181675, 109.47348838]
Option 1
Direct assignment.
选项 1
直接分配。
df['b'] = vals
print(df)
a b
Index
0 0.671399 137.550212
35 0.446172 125.300177
63 0.614758 130.201817
72 0.634448 109.473488
Option 2df.assign
选项 2df.assign
df = df.assign(b=vals)
print(df)
a b
Index
0 0.671399 137.550212
35 0.446172 125.300177
63 0.614758 130.201817
72 0.634448 109.473488
Option 3df.fillna
选项 3df.fillna
df.b = df.b.fillna(pd.Series(vals, index=df.index))
print(df)
a b
Index
0 0.671399 137.550212
35 0.446172 125.300177
63 0.614758 130.201817
72 0.634448 109.473488
If your values are Nan(string) instead of NaN(float), you can convert it, using df.replace:
如果您的值是Nan(string) 而不是NaN(float)，您可以使用df.replace以下命令对其进行转换：
df = df.replace('Nan', np.nan)
