Dictionary comprehension - Get the values for each column in a dataframe-CodePudding

Context: I'd like to create a dictionary with "Variable:values" pairs. The variable has a multindex like this:

   Sexo
   Masculino Feminino
0   4245.0  4620.0
1   4274.0  4655.0
2   4304.5  4689.5
3   4322.0  4708.5
4   4318.0  4717.5
5   4288.0  4710.5
6   4247.5  4683.5
7   4224.5  4650.5
8   4223.5  4613.5
9   4191.0  4567.0
10  4133.7  4546.9
11  4093.6  4550.1
12  4079.0  4551.5
13  4070.5  4562.6

And I'm creating the dictionary using a dict comprehension:

dict_boxplot = {col:val for (col,val) in zip(subcolumns,df_var.iloc[:,1])}

Where subcolumns are simply the names "Masculino" and "Feminino". How cold I make a dictionary with this format : {'Masculino': [value1,value2,value3...], 'Feminino': [value1,value2,value3...]} where value1, etc are the values from each column?

Thank you in advance!

CodePudding user response：

Iterate only on column names, then collect the column and convert to a list

x = {col: df[col].to_list() for col in ['Masculino', 'Feminino']}

Or use DataFrame.to_dict to help you

x = {k: list(column.values()) for k, column in df.to_dict().items()}

Helper code

import pandas as pd
values = {'Masculino': [4245.0, 4274.0, 4304.5, 4322.0, 4318.0, 4288.0, 4247.5,
                        4224.5, 4223.5, 4191.0, 4133.7, 4093.6, 4079.0, 4070.5],
          'Feminino': [4620.0, 4655.0, 4689.5, 4708.5, 4717.5, 4710.5, 4683.5,
                       4650.5, 4613.5, 4567.0, 4546.9, 4550.1, 4551.5, 4562.6]}
df = pd.DataFrame(values)

x = {col: df[col].to_list() for col in ['Masculino', 'Feminino']}
print(x)  # {'Masculino': [4245.0, ..., 4070.5], 'Feminino': [4620.0, ..., 4562.6]}

x = {k: list(column.values()) for k, column in df.to_dict().items()}
print(x)  # {'Masculino': [4245.0, ..., 4070.5], 'Feminino': [4620.0, ..., 4562.6]}

CodePudding user response：

Simply use to_dict('list'):

df.to_dict('list')

Output:

{'Masculino': [4245.0, 4274.0, 4304.5, 4322.0, 4318.0, 4288.0, 4247.5, 4224.5, 4223.5, 4191.0, 4133.7, 4093.6, 4079.0, 4070.5],
 'Feminino': [4620.0, 4655.0, 4689.5, 4708.5, 4717.5, 4710.5, 4683.5, 4650.5, 4613.5, 4567.0, 4546.9, 4550.1, 4551.5, 4562.6]
}