Creating multiple data frames using for loop with conditions

Question

I have two data frames.

DF1

col1           col2
price($)       price(#)    
dimension(m)   dimension(inch)      
color1         color2

DF2


toyname   price($)     price(#)   dimension(m)   dimension(inch)  color1   color2
t1           2            12          11               21            gr       re 
t2           3            13          10               20            bl       re 
t3           12           23          20               30            ye       bl

I am looking for making individual dataframes for each toyname from DF2 using template of DF1:

t1
col1           col2
2               12
11              21
gr              re

t2
col1           col2
3               13
10              20
bl              re

Henry Ecker · Accepted Answer · 2021-06-22 16:55:09Z

1

Assuming each row represents a different toy. Try with set_index + iterrows + applymap:

dfs = {}
for i, row in df2.set_index('toyname').iterrows():
    dfs[i] = df1.applymap(lambda col: row[col])

dfs:

{'t1':   col1 col2
0    2   12
1   11   21
2   gr   re, 
't2':   col1 col2
0    3   13
1   10   20
2   bl   re, 
't3':   col1 col2
0   12   23
1   20   30
2   ye   bl}

dfs['t1']:

  col1 col2
0    2   12
1   11   21
2   gr   re

DataFrames Used:

df1 = pd.DataFrame({
    'col1': {0: 'price($)', 1: 'dimension(m)', 2: 'color1'},
    'col2': {0: 'price(#)', 1: 'dimension(inch)', 2: 'color2'}
})

df2 = pd.DataFrame({
    'toyname': {0: 't1', 1: 't2', 2: 't3'}, 'price($)': {0: 2, 1: 3, 2: 12},
    'price(#)': {0: 12, 1: 13, 2: 23}, 'dimension(m)': {0: 11, 1: 10, 2: 20},
    'dimension(inch)': {0: 21, 1: 20, 2: 30},
    'color1': {0: 'gr', 1: 'bl', 2: 'ye'},
    'color2': {0: 're', 1: 're', 2: 'bl'}
})

answered Jun 22, 2021 at 16:55

Henry Ecker♦

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2 Comments

Kathan Vyas Over a year ago

how do i access individual element for dfs['t1']?

Henry Ecker Over a year ago

dfs['t1'] is a dataframe so the same way you would with any other dataframe. dfs['t1']['col1'] to get a column or dfs['t1'].loc[0, 'col1'] for the value at index 0 for col1 etc.

Vishnudev Krishnadas · Accepted Answer · 2021-06-22 17:19:40Z

0

A simple one-liner using dict comprehension would be

dfs = {row.get('toyname'): df1.applymap(row.get) for row in df2.to_dict('records')}

print(dfs.get('t1'))

Output

  col1 col2
0    2   12
1   11   21
2   gr   re

To access data from a dataframe

data.get('t1').loc[0, 'col1']
2

answered Jun 22, 2021 at 17:19

Vishnudev Krishnadas

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