Question

（可复制的示例和最后的尝试不力）

我有两个数据帧df1和df2：

df1：

    Col_A   Col_B   Col_D
1   NaN     21      NaN
2   10      NaN     33
4   12      23      38

df2：

    Col_C   Col_E
2   22      44
3   NaN     45
5   4       48

我想找到一种常见的形式：

df_common：

    Col_A   Col_B   Col_C   Col_D   Col_E
1   NaN     NaN     NaN     NaN     NaN
2   NaN     NaN     NaN     NaN     NaN
3   NaN     NaN     NaN     NaN     NaN
4   NaN     NaN     NaN     NaN     NaN
5   NaN     NaN     NaN     NaN     NaN

...我拥有所有列名和行索引的union，并且只有NaN值：

然后我想填写df1和df2的值（仍在两个单独的表中），以便最终得到：

df1_desired

    Col_A   Col_B   Col_C   Col_D   Col_E
1   NaN     21      NaN     NaN     NaN
2   10      NaN     NaN     33      NaN
3   NaN     NaN     NaN     NaN     NaN
4   12      23      NaN     NaN     NaN
5   NaN     NaN     NaN     38      NaN

df2_resired：

    Col_A   Col_B   Col_C   Col_D   Col_E
1   NaN     NaN     NaN     NaN     NaN
2   NaN     NaN     22      NaN     44
3   NaN     NaN     NaN     NaN     35
4   NaN     NaN     NaN     NaN     NaN
5   NaN     NaN     4       NaN     48

我尝试了pd.merge()和df.update()的各种尝试，但均未成功

但是我已经接受了这样的事实，我什至不知道该如何称呼这个特殊挑战。谢谢您的任何建议！

可复制的示例：

import pandas as pd
import numpy as np

df1 = pd.DataFrame({'Col_A': {1: np.nan, 2: '10', 4: '12'},
        'Col_B': {1: '21', 2: np.nan, 4: '23'},
        'Col_D': {1: np.nan, 2: '33', 4: '38'}})

df2 = pd.DataFrame({'Col_C': {2: '22', 3: np.nan, 5: '4'},
                    'Col_E': {2: 44, 3: 45, 5: 48}})

df1_desired = pd.DataFrame({'Col_A': {1: np.nan, 2: '10', 3: np.nan, 4: '12', 5: np.nan},
                        'Col_B': {1: '23', 2: np.nan, 3: np.nan, 4: '23', 5: np.nan},
                        'Col_C': {1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan, 5: np.nan},
                        'Col_D': {1: np.nan, 2: '22', 3: np.nan, 4: np.nan, 5: '4'},
                        'Col_E': {1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan, 5: np.nan}})

df2_desired = pd.DataFrame({'Col_A': {1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan, 5: np.nan},
                        'Col_B': {1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan, 5: np.nan},
                        'Col_C': {1: np.nan, 2: '22', 3: np.nan, 4: np.nan, 5: '4'},
                        'Col_D': {1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan, 5: np.nan},
                        'Col_E': {1: np.nan, 2: '44', 3: '35', 4: np.nan, 5: '48'}})

# find the commons
common_cols = sorted(list(set().union(list(df1),list(df2))))
common_rows = sorted(list(set().union(list(df1.index),list(df2.index))))
df_common = pd.DataFrame(np.nan, index=common_rows, columns=common_cols)

# attempt at reshaping df1 with pd.merge
# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html
df1_reshaped = pd.merge(df_common, df1, how='left', left_index=True, right_index=True)

# attempt at dropping duplicates for df1
#df1_reshaped = df1_reshaped[df1_reshaped.columns.drop(list(df1_reshaped.filter(regex='_x')))]
#df1_reshaped.columns = df_common.columns

# attempt with df.update()
# https://stackoverflow.com/questions/9787853/join-or-merge-with-overwrite-in-pandas
df1_updated=df_common.update(df1)

Answer 1

您可以使用：

ttemp <- function(){
  df <- read.csv("/Untitled 3.csv")
  df[,3:6] <- apply(df[,3:6],2,norm)
}

norm <- function(x, maxVal){
  min = 0
  y <- (x-min)/(maxVal-min)
  return(y)
}

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