python sort dictionary of lists based on single list

Question

I have imported a csv file and stored its data into a dictionary with pandas as follows:

import pandas as pd
df = pd.read_csv(inputfile)
mydict = df.to_dict(orient='list')

Now, I would like to use one list to

set new keys
sort other lists

I have achieved something similar by doing so:

gb = df.groupby('time')
ts = dict(list(gb))

The "problem" is that I have a dictionray of dataframes instead of a dictionray of lists.

BASIC EXAMPLE

csv file:

var1 var2 var3 time var4
1    0    1.1  0    34
2    0    2.1  1    150
3    0    6    2    16
1    0    1.1  0    34
2    0    2.1  1    150
3    0    6    2    16
1    0    1.1  0    34
2    0    2.1  1    150
3    0    6    2    16

dictionary imported with pandas

mydict = 
{'var1': [1, 2, 3, 1, 2, 3, 1, 2, 3], 
'var2': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
'var3': [1.1, 2.1, 6.0, 1.1, 2.1, 6.0, 1.1, 2.1, 6.0], 
'time': [0, 1, 2, 0, 1, 2, 0, 1, 2], 
'var4': [34, 150, 16, 34, 150, 16, 34, 150, 16]}

what I would like to achieve is

mydict2 = 
{0: {'var1':[1,1,1], 'var2':[0,0,0], 'var3':[1.1,1.1,1.1], 'var4':[34,34,34]},
 1: {'var1':[2,2,2], 'var2':[0,0,0], 'var3':[2.1,2.1,2.1], 'var4':[150,150,150]},
 2: {'var1':[3,3,3], 'var2':[0,0,0], 'var3':[6,6,6], 'var4':[16,16,16]}}

So far what I have obtained is similar, but for each time I get a dataframe:

ts = {0:    var1  var2  var3  time  var4
0     1     0   1.1     0    34
3     1     0   1.1     0    34
6     1     0   1.1     0    34, 
1:    var1  var2  var3  time  var4
1     2     0   2.1     1   150
4     2     0   2.1     1   150
7     2     0   2.1     1   150, 
2:    var1  var2  var3  time  var4
2     3     0   6.0     2    16
5     3     0   6.0     2    16
8     3     0   6.0     2    16}

where, if I type type(ts[0]) I get <class 'pandas.core.frame.DataFrame'> which is ok (I can get the job done), but what if I wanted a dictionart of lists?

Andrej Kesely · Accepted Answer · 2021-05-20 09:01:59Z

1

You can do:

x = df.groupby("time").agg(list).to_dict("index")
print(x)

Prints:

{0: {'var1': [1, 1, 1], 'var2': [0, 0, 0], 'var3': [1.1, 1.1, 1.1], 'var4': [34, 34, 34]}, 
 1: {'var1': [2, 2, 2], 'var2': [0, 0, 0], 'var3': [2.1, 2.1, 2.1], 'var4': [150, 150, 150]}, 
 2: {'var1': [3, 3, 3], 'var2': [0, 0, 0], 'var3': [6.0, 6.0, 6.0], 'var4': [16, 16, 16]}}

answered May 20, 2021 at 9:01

Andrej Kesely

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jezrael · Accepted Answer · 2021-05-20 09:07:28Z

1

Use dict comprehension with orient='list' in DataFrame.to_dict:

d = {k: v.to_dict(orient='list') for k, v in df.set_index('time').groupby('time')}
print (d)
{0: {'var1': [1, 1, 1], 'var2': [0, 0, 0], 'var3': [1.1, 1.1, 1.1], 'var4': [34, 34, 34]}, 
 1: {'var1': [2, 2, 2], 'var2': [0, 0, 0], 'var3': [2.1, 2.1, 2.1], 'var4': [150, 150, 150]}, 
 2: {'var1': [3, 3, 3], 'var2': [0, 0, 0], 'var3': [6.0, 6.0, 6.0], 'var4': [16, 16, 16]}}

edited May 20, 2021 at 9:07

answered May 20, 2021 at 9:01

jezrael

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