用新的数据框替换行 [英] Replace a row by a new Dataframe

问题描述

我正在寻找一种更优雅的方法来从字典的值替换另一个数据帧中的数据帧.

I am looking for a more elegant way to replace a dataframe in another dataframe from the values of a dictionary.

这是我必须使用的数据类型的示例

here its an example of the type of data i have to use

d = {1 : {'name' : 'bob','age' : 22,'Data' : {}},
 4 : {'name' : 'sam','age' : 30,'Data' : {}},
 2 : {'name' : 'tom','age' : 20,'Data' : [{'Mail':'B','MailValue': 89},
                                          {'Mail':'C','MailValue' : 100}]},
 3 : {'name' : 'mat','age' : 19,'Data' : [{'Mail':'D','MailValue': 71}]}}                                     '
df = pd.DataFrame(d).T
df
                                                 Data age name
1                                                 {}  22  bob
4                                                 {}  30  sam
2  [{'Mail': 'B', 'MailValue': 89}, {'Mail': 'C',...  20  tom
3                   [{'Mail': 'D', 'MailValue': 71}]  19  mat

这是我对最终单元格df2中的数据单元格的附加值以及复制名称和年龄列的实际解决方案

here is my actual solution for append value of Data cell and replicate name and age columns in the final dataframe df2

df2 = pd.DataFrame()
for idx, row in df[:].iterrows():
    dfx = pd.DataFrame(row.Data)
    dfx['idx'] = idx
    df2 = df2.append(dfx)

df2.set_index('idx', inplace= True)
df2[df.columns] = df
df2 = df2.append(df.drop(df2.index.unique())).drop(columns = ['Data'])

print(df2)
  Mail  MailValue age name
2    B       89.0  20  tom
2    C      100.0  20  tom
3    D       71.0  19  mat
1  NaN        NaN  22  bob
4  NaN        NaN  30  sam

推荐答案

一种方法是将pd.concat与可迭代的拆分数据帧一起使用，注意为空字典构造单行数据帧:

One way is to use pd.concat with an iterable of split dataframes, taking care to construct a one-row dataframe for empty dictionaries:

splits = [pd.DataFrame(x if x else [{}]) for x in df.pop('Data')]

lens = list(map(len, splits))

df = pd.DataFrame({'age': np.repeat(df['age'].values, lens),
                   'name': np.repeat(df['name'].values, lens)})\
       .join(pd.concat(splits, ignore_index=True))


print(df)
#   age name Mail  MailValue
# 0  22  bob  NaN        NaN
# 1  20  tom    B       89.0
# 2  20  tom    C      100.0
# 3  19  mat    D       71.0
# 4  30  sam  NaN        NaN

这篇关于用新的数据框替换行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！