python - 跨多个 pandas 数据帧设置 nans

Question

我有许多类似的数据帧，我想在所有数据帧中标准化 nans。例如，如果 nan 存在于 df1.loc[0,'a'] 中，那么对于相同的索引位置，所有其他数据帧都应设置为 nan。

我知道我可以将数据框分组以创建一个大型多索引数据框，但有时我发现使用一组相同结构的数据框更容易。

这是一个例子:

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.reshape(np.arange(12), (4,3)), columns=['a', 'b', 'c'])
df2 = pd.DataFrame(np.reshape(np.arange(12), (4,3)), columns=['a', 'b', 'c'])
df3 = pd.DataFrame(np.reshape(np.arange(12), (4,3)), columns=['a', 'b', 'c'])

df1.loc[3,'a'] = np.nan
df2.loc[1,'b'] = np.nan
df3.loc[0,'c'] = np.nan

print df1
print ' ' 
print df2
print ' ' 
print df3

输出:

     a   b   c
0  0.0   1   2
1  3.0   4   5
2  6.0   7   8
3  NaN  10  11

   a     b   c
0  0   1.0   2
1  3   NaN   5
2  6   7.0   8
3  9  10.0  11

   a   b     c
0  0   1   NaN
1  3   4   5.0
2  6   7   8.0
3  9  10  11.0

但是，我希望 df1、df2 和 df3 在相同位置有 nan:

print df1
     a     b     c
0  0.0   1.0   NaN
1  3.0   NaN   5.0
2  6.0   7.0   8.0
3  NaN  10.0  11.0

---

使用 piRSquared 提供的答案，我能够将其扩展为不同大小的数据帧。这是函数:

def set_nans_over_every_df(df_list):
    # Find unique index and column values
    complete_index = sorted(set([idx for df in df_list for idx in df.index]))
    complete_columns = sorted(set([idx for df in df_list for idx in df.columns]))

    # Ensure that every df has the same indexes and columns
    df_list = [df.reindex(index=complete_index, columns=complete_columns) for df in df_list]

    # Find the nans in each df and set nans in every other df at the same location     
    mask = np.isnan(np.stack([df.values for df in df_list])).any(0)
    df_list = [df.mask(mask) for df in df_list]

    return df_list

以及使用不同大小的数据框的示例:

df1 = pd.DataFrame(np.reshape(np.arange(15), (5,3)), index=[0,1,2,3,4], columns=['a', 'b', 'c'])
df2 = pd.DataFrame(np.reshape(np.arange(12), (4,3)), index=[0,1,2,3], columns=['a', 'b', 'c'])
df3 = pd.DataFrame(np.reshape(np.arange(16), (4,4)), index=[0,1,2,3], columns=['a', 'b', 'c', 'd'])

df1.loc[3,'a'] = np.nan
df2.loc[1,'b'] = np.nan
df3.loc[0,'c'] = np.nan

df1, df2, df3 = set_nans_over_every_df([df1, df2, df3])

print df1

     a     b     c   d
0  0.0   1.0   NaN NaN
1  3.0   NaN   5.0 NaN
2  6.0   7.0   8.0 NaN
3  NaN  10.0  11.0 NaN
4  NaN   NaN   NaN NaN

Answer 1

我会在 numpy 中设置一个 mask 然后在 pd.DataFrame.mask 中使用这个 mask > 方法

mask = np.isnan(np.stack([d.values for d in [df1, df2, df3]])).any(0)

---

print(df1.mask(mask))

     a     b     c
0  0.0   1.0   NaN
1  3.0   NaN   5.0
2  6.0   7.0   8.0
3  NaN  10.0  11.0

---

print(df2.mask(mask))

     a     b     c
0  0.0   1.0   NaN
1  3.0   NaN   5.0
2  6.0   7.0   8.0
3  NaN  10.0  11.0

---

print(df3.mask(mask))

     a     b     c
0  0.0   1.0   NaN
1  3.0   NaN   5.0
2  6.0   7.0   8.0
3  NaN  10.0  11.0