在python中使用列的动态列表按列拆分数据帧

Question

Very new python user here. I have a data frame that I am trying to subset by whatever unique values that are in the column "Level". I would like each sub-setting result in a list or its own data frame. In this example I have Level 1, 2, 3, 4, 5 so I would want either 5 separate data frames with only one unique value in each data frame or a list with 5 different values. Here is the data frame:

Using Python 3.7

import pandas as pd
import numpy as np

data = [['Bill', 21, 'Level 1'], ['Joe', 25, 'Level 1'],['Sam', 22, 'Level 2'],['Ash', 19, 'Level 3'],['Mike', 28, 'Level 3'],['Ang', 20, 'Level 4'],['Paul', 25, 'Level 4'],['Kathy', 29, 'Level 5']]

df = pd.DataFrame(data, columns = ['Name', 'Age', 'Level'])

I can do get the desired results if I know the names of the different levels and can hard code it into the code. My problem is I do not always know what will be in the 'Level' column. The code would need to be smart enough to detect the different levels, split by that, and saved the result in a data frames or a list. I am not really sure how to go about getting this started..

Thank you!

Answer 1

Take a look if this solves your problem

To get all the unique levels in your data:

df = pd.DataFrame(data, columns = ['Name', 'Age', 'Level'])
levels = list()
levels = df['Level']
levels = set(levels)
print(levels) # gives you all the unique levels (1 to 5)

To get data of each level (all together):

data = [['Bill', 21, 'Level 1'], ['Joe', 25, 'Level 1'],['Sam', 22, 'Level 2'],['Ash', 19, 'Level 3'],['Mike', 28, 'Level 3'],['Ang', 20, 'Level 4'],['Paul', 25, 'Level 4'],['Kathy', 29, 'Level 5']]
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Level'])
levels = list()
levels = df['Level']
levels = set(levels)  ## gets unique levels {'Level 1', 'Level 2', 'Level 3', 'Level 4', 'Level 5'}
for l in levels:
    df_level = df.loc[df['Level'] == l]
    print("Data for Level:"+l)
    print(df_level[['Name','Age']])
    print("======================")

Output

Data for Level:Level 4
   Name  Age
5   Ang   20
6  Paul   25
======================
Data for Level:Level 5
    Name  Age
7  Kathy   29
======================
Data for Level:Level 3
   Name  Age
3   Ash   19
4  Mike   28
======================
Data for Level:Level 1
   Name  Age
0  Bill   21
1   Joe   25
======================
Data for Level:Level 2
  Name  Age
2  Sam   22
======================