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Sort and concatenate the dataframes

Tags:

python

sorting

concatenation

pandas

dataframe

I have following two dataframes:

>>> df1
  c1  c2  v1  v2
0  A NaN   9   2
1  B NaN   2   5
2  C NaN   3   5
3  D NaN   4   2

>>> df2
   c1 c2  v1  v2
0   A  P   4   1
1   A  T   3   1
2   A  Y   2   0
3   B  P   0   1
4   B  T   2   2
5   B  Y   0   2
6   C  P   1   2
7   C  T   1   2
8   C  Y   1   1
9   D  P   1   1
10  D  T   2   0
11  D  Y   1   1

I need to concatenate the dataframes and sort them or vice versa. The first dataframe needs to be sorted on v1 column, then the second dataframe needs to be sorted based on the order of the values from c1 column after sorting the first dataframe, and the v2 column from the second dataframe.

A working version is something like this: sorting first dataframe on v1, then iterating the rows, and filtering the second dataframe for the value of c2 column, and sorting the filtered second dataframe on v2, finally concatenating all the frames.

result = []
for i,row in df1.sort_values('v1').iterrows():
    result.append(row.to_frame().T)
    result.append(df2[df2['c1'].eq(row['c1'])].sort_values('v2'))

The resulting dataframe after sorting:

>>> pd.concat(result, ignore_index=True)
   c1   c2 v1 v2
0   B  NaN  2  5
1   B    P  0  1
2   B    T  2  2
3   B    Y  0  2
4   C  NaN  3  5
5   C    Y  1  1
6   C    P  1  2
7   C    T  1  2
8   D  NaN  4  2
9   D    T  2  0
10  D    P  1  1
11  D    Y  1  1
12  A  NaN  9  2
13  A    Y  2  0
14  A    P  4  1
15  A    T  3  1

The problem with above approach is its iterative, and not so efficient when the number of dataframes increases and/or the number of rows increases in these dataframes. The real use-case scenario has from 2 to 6 dataframes, where number of rows ranges from few thousands to hundred thousands.

UPDATE:

Either of sorting the dataframes first then concatenating them, or concatenating the datframes first then sorting, will be fine, that is why I just included both the dataframes instead of just concatenating them and presenting a single dataframe.

EDIT:

Here is 4 dataframes from actual use-case scenario:

from  math import nan
import pandas as pd

df4 = pd.DataFrame({'c1': ['BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT'], 'c2': ['D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2', 'w2'], 'c3': ['BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH', 'BAF', 'BAF', 'BAF', 'BAF', 'BAF', 'WH', 'WH', 'WH', 'WH', 'WH'], 'c4': ['001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss', '001', '002', '003', '004', 'mss'], 'v1': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 4, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 4, 6, 4, 0, 2, 2, 0, 2, 0, 2, 4, 6, 4, 0, 2, 2, 0, 1, 0, 2, 3, 6, 2, 0, 2, 2, 0, 1, 0, 1, 3, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 4, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 4, 6, 4, 0, 2, 2, 0, 2, 0, 2, 4, 6, 4, 0, 2, 2, 0, 1, 0, 2, 3, 6, 2, 0, 2, 2, 0, 1, 0, 1, 3, 5, 1, 0, 2, 2, 0, 2, 0, 2, 4, 6, 4, 0, 2, 2, 0, 2, 0, 2, 4, 6, 4, 0, 2, 2, 0, 1, 0, 2, 3, 6, 2, 0, 2, 2, 0, 1, 0, 1, 3, 5, 1, 0], 'v2': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 2, 4, 6, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 2, 4, 6, 5, 0, 0, 1, 0, 1, 0, 2, 4, 6, 5, 0, 0, 0, 0, 1, 0, 2, 3, 5, 4, 0, 0, 0, 0, 1, 0, 1, 3, 5, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 2, 4, 6, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 2, 4, 6, 5, 0, 0, 1, 0, 1, 0, 2, 4, 6, 5, 0, 0, 0, 0, 1, 0, 2, 3, 5, 4, 0, 0, 0, 0, 1, 0, 1, 3, 5, 3, 0, 0, 1, 0, 1, 0, 2, 4, 6, 5, 0, 0, 1, 0, 1, 0, 2, 4, 6, 5, 0, 0, 0, 0, 1, 0, 2, 3, 5, 4, 0, 0, 0, 0, 1, 0, 1, 3, 5, 3, 0], 'v3': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 5, 9, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 5, 9, 7, 0, 1, 2, 1, 0, 0, 1, 5, 9, 7, 0, 1, 2, 1, 0, 0, 0, 4, 6, 4, 0, 1, 2, 1, 0, 0, 0, 2, 6, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 5, 9, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 5, 9, 7, 0, 1, 2, 1, 0, 0, 1, 5, 9, 7, 0, 1, 2, 1, 0, 0, 0, 4, 6, 4, 0, 1, 2, 1, 0, 0, 0, 2, 6, 3, 0, 1, 2, 1, 0, 0, 1, 5, 9, 7, 0, 1, 2, 1, 0, 0, 1, 5, 9, 7, 0, 1, 2, 1, 0, 0, 0, 4, 6, 4, 0, 1, 2, 1, 0, 0, 0, 2, 6, 3, 0], 'v4': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 1, 3, 0, 5, 13, 21, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 1, 3, 0, 5, 13, 21, 16, 0, 3, 5, 1, 3, 0, 5, 13, 21, 16, 0, 3, 4, 1, 2, 0, 4, 10, 17, 10, 0, 3, 4, 1, 2, 0, 2, 8, 16, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 1, 3, 0, 5, 13, 21, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 1, 3, 0, 5, 13, 21, 16, 0, 3, 5, 1, 3, 0, 5, 13, 21, 16, 0, 3, 4, 1, 2, 0, 4, 10, 17, 10, 0, 3, 4, 1, 2, 0, 2, 8, 16, 7, 0, 3, 5, 1, 3, 0, 5, 13, 21, 16, 0, 3, 5, 1, 3, 0, 5, 13, 21, 16, 0, 3, 4, 1, 2, 0, 4, 10, 17, 10, 0, 3, 4, 1, 2, 0, 2, 8, 16, 7, 0]})
df3 = pd.DataFrame({'c1': ['BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT'], 'c2': ['D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2', 'D1', 'D1', 'D1', 'Sc', 'Sc', 'Sc', 'w1', 'w1', 'w1', 'w2', 'w2', 'w2'], 'c3': ['BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss', 'BAF', 'WH', 'mss'], 'c4': [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], 'v1': [0, 0, 0, 6, 16, 0, 0, 0, 0, 0, 0, 0, 6, 16, 0, 6, 16, 0, 5, 13, 0, 5, 10, 0, 0, 0, 0, 6, 16, 0, 0, 0, 0, 0, 0, 0, 6, 16, 0, 6, 16, 0, 5, 13, 0, 5, 10, 0, 6, 16, 0, 6, 16, 0, 5, 13, 0, 5, 10, 0], 'v2': [0, 0, 0, 2, 17, 0, 0, 0, 0, 0, 0, 0, 2, 17, 0, 2, 17, 0, 1, 14, 0, 1, 12, 0, 0, 0, 0, 2, 17, 0, 0, 0, 0, 0, 0, 0, 2, 17, 0, 2, 17, 0, 1, 14, 0, 1, 12, 0, 2, 17, 0, 2, 17, 0, 1, 14, 0, 1, 12, 0], 'v3': [0, 0, 0, 4, 22, 0, 0, 0, 0, 0, 0, 0, 4, 22, 0, 4, 22, 0, 4, 14, 0, 4, 11, 0, 0, 0, 0, 4, 22, 0, 0, 0, 0, 0, 0, 0, 4, 22, 0, 4, 22, 0, 4, 14, 0, 4, 11, 0, 4, 22, 0, 4, 22, 0, 4, 14, 0, 4, 11, 0], 'v4': [0, 0, 0, 12, 55, 0, 0, 0, 0, 0, 0, 0, 12, 55, 0, 12, 55, 0, 10, 41, 0, 10, 33, 0, 0, 0, 0, 12, 55, 0, 0, 0, 0, 0, 0, 0, 12, 55, 0, 12, 55, 0, 10, 41, 0, 10, 33, 0, 12, 55, 0, 12, 55, 0, 10, 41, 0, 10, 33, 0]})
df2 = pd.DataFrame({'c1': ['BMI', 'BMI', 'BMI', 'BMI', 'BMI', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'DIABP', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'HEIGHT', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'SYSBP', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT', 'WEIGHT'], 'c2': ['D1', 'Sc', 'w1', 'w2', 'mss', 'D1', 'Sc', 'w1', 'w2', 'mss', 'D1', 'Sc', 'w1', 'w2', 'mss', 'D1', 'Sc', 'w1', 'w2', 'mss', 'D1', 'Sc', 'w1', 'w2', 'mss'], 'c3': [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], 'c4': [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], 'v1': [0, 22, 0, 0, 0, 22, 22, 18, 15, 0, 0, 22, 0, 0, 0, 22, 22, 18, 15, 0, 22, 22, 18, 15, 0], 'v2': [0, 19, 0, 0, 0, 19, 19, 15, 13, 0, 0, 19, 0, 0, 0, 19, 19, 15, 13, 0, 19, 19, 15, 13, 0], 'v3': [0, 26, 0, 0, 0, 26, 26, 18, 15, 0, 0, 26, 0, 0, 0, 26, 26, 18, 15, 0, 26, 26, 18, 15, 0], 'v4': [0, 67, 0, 0, 0, 67, 67, 51, 43, 0, 0, 67, 0, 0, 0, 67, 67, 51, 43, 0, 67, 67, 51, 43, 0]})
df1 = pd.DataFrame({'c1': ['BMI', 'DIABP', 'HEIGHT', 'SYSBP', 'WEIGHT', 'mss'], 'c2': [nan, nan, nan, nan, nan, nan], 'c3': [nan, nan, nan, nan, nan, nan], 'c4': [nan, nan, nan, nan, nan, nan], 'v1': [22, 22, 22, 22, 22, 0], 'v2': [19, 19, 19, 19, 19, 0], 'v3': [26, 26, 26, 26, 26, 0], 'v4': [67, 67, 67, 67, 67, 0]})

# Comment for easy code selection

Even for above four dataframes, sorting and merging criteria is still the same

  • Sorting df1 on v1
  • Sorting c2 in df2 on v2, maintaining the order of c1 from df1
  • Sorting c3 in df3 on v3, maintaining the order of c1 from df1, and c2 from df2
  • Sorting c4 in df3 on v4, maintaining the order of c1 from df1, c2 from df2, and c3 from df3

And in such cases when the number of dataframe to sort and merge grows, the solution I have used above is becoming really inefficient.

like image 958
ThePyGuy Avatar asked Aug 21 '21 21:08

ThePyGuy

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Video Answer

3 Answers

Another solution using groupby without sorting groups:

import itertools

out = pd.concat([df1.sort_values('v1'),
                 df2.sort_values('v2')],
                 ignore_index=True)

# Original answer
# >>> out.reindex(out.groupby('c1', sort=False)
#         .apply(lambda x: x.index)
#         .explode())

# Faster alternative
>>> out.loc[itertools.chain.from_iterable(out.groupby('c1', sort=False)
                                             .groups.values())]

>>> out
   c1   c2  v1  v2
0   B  NaN   2   5
8   B    P   0   1
12  B    T   2   2
13  B    Y   0   2
1   C  NaN   3   5
9   C    Y   1   1
14  C    P   1   2
15  C    T   1   2
2   D  NaN   4   2
5   D    T   2   0
10  D    P   1   1
11  D    Y   1   1
3   A  NaN   9   2
4   A    Y   2   0
6   A    P   4   1
7   A    T   3   1
like image 67
Corralien Avatar answered Sep 29 '22 16:09

Corralien

Here is another way which might help you:

  1. Create a rank dictionary with c1 as keys and rank of v1 as values
  2. Concat both the dataframes with keys as 1 and 2 (This will help prioritizing the left dataframe)
  3. Assign a helper rank column to the concated dataframe and then sort on that, then Key and then v2.

Method 1:

def mysort(df1,df2):
    d = dict(zip(df2['c1'],df2['v1'].rank()))
    o = pd.concat((df2,df1),keys=[1,2],names=['Key'])

    return (o.assign(k=o['c1'].map(d)).sort_values(['k','Key','v2'])
            .loc[:,list(df1)])#.reset_index(drop=True)

Method 2:

I think should work faster using the same logic but sorting with lexsort:

def mysort_two(df1,df2):
    d  = dict(zip(df2['c1'],df2['v1'].rank()))
    o = pd.concat((df2,df1),keys=[1,2],names=['Key'])
    a = o.to_numpy()[np.lexsort((o['v2'],o.index.get_level_values('Key'),
                o['c1'].map(d)))]
    return pd.DataFrame(a,columns=df2.columns)

#Same can also be written as below:
# def mysort_two(df1,df2):
#     d  = dict(zip(df2['c1'],df2['v1'].rank()))
#     o = pd.concat((df2,df1))
#     a = o.to_numpy()[np.lexsort((o['v2']
#                 ,np.append(np.ones(len(df2)), np.ones(len(df1))*2),
#                 o['c1'].map(d)))]
#     return pd.DataFrame(a,columns=df2.columns)

print(mysort_two(df1,df2)) #method2
#print(mysort(df1,df2)) #method1

   c1   c2  v1  v2
0   B  NaN   2   5
1   B    P   0   1
2   B    T   2   2
3   B    Y   0   2
4   C  NaN   3   5
5   C    Y   1   1
6   C    P   1   2
7   C    T   1   2
8   D  NaN   4   2
9   D    T   2   0
10  D    P   1   1
11  D    Y   1   1
12  A  NaN   9   2
13  A    Y   2   0
14  A    P   4   1
15  A    T   3   1
like image 28
anky Avatar answered Sep 29 '22 17:09

anky

Assuming df2's c1 column contains unique values in c1 (as in OP) we can try establishing categorical ordering in c1 based on the sorted values of v1 in df2. Adding indicator values to each DataFrame, then concating and sorting based on the new categorical type (c1), indicator, and v1.

# Establish ordering based on sorted df2
cat_type = pd.CategoricalDtype(df2.sort_values('v1')['c1'], ordered=True)
new_df = (
    # Add Indicator to each DataFrame
    pd.concat([df.assign(indicator=i) for (i, df) in enumerate([df1, df2])])
        # Set c1 to the categorical ordering from above
        .astype({'c1': cat_type})
        # Sort By Categorical, then df2 first then v2 within dfs
        .sort_values(['c1', 'indicator', 'v2'],
                     ascending=(True, False, True),
                     ignore_index=True)
        # Remove Indicator column
        .drop(columns='indicator')
)

new_df:

   c1   c2  v1  v2
0   B  NaN   2   5
1   B    P   0   1
2   B    T   2   2
3   B    Y   0   2
4   C  NaN   3   5
5   C    Y   1   1
6   C    P   1   2
7   C    T   1   2
8   D  NaN   4   2
9   D    T   2   0
10  D    P   1   1
11  D    Y   1   1
12  A  NaN   9   2
13  A    Y   2   0
14  A    P   4   1
15  A    T   3   1

Setup:

import pandas as pd
from numpy import nan

df1 = pd.DataFrame({
    'c1': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C', 'D', 'D', 'D'],
    'c2': ['P', 'T', 'Y', 'P', 'T', 'Y', 'P', 'T', 'Y', 'P', 'T', 'Y'],
    'v1': [4, 3, 2, 0, 2, 0, 1, 1, 1, 1, 2, 1],
    'v2': [1, 1, 0, 1, 2, 2, 2, 2, 1, 1, 0, 1]
})

df2 = pd.DataFrame({
    'c1': ['A', 'B', 'C', 'D'],
    'c2': [nan, nan, nan, nan],
    'v1': [9, 2, 3, 4],
    'v2': [2, 5, 5, 2]
})

Some timing information:

OP

%timeit op_fn(df1, df2)
4.93 ms ± 376 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

This answer

%timeit cat_ordered(df1, df2)
3.72 ms ± 490 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

@Andrej Kesely's answer

%timeit groupby_fn(df1, df2)
7.79 ms ± 140 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

@Corralien's answer

%timeit concat_itertools(df1, df2)
1.48 ms ± 82.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

@Scott Boston's answer

%timeit helpcol(df1, df2)
3.05 ms ± 44.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

@U12-Forward's answer

%timeit U11(df1, df2)
5.41 ms ± 255 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

@Anky's answers

%timeit mysort_anky(df1, df2)
3.05 ms ± 180 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit mysort_two_anky(df1, df2)
1.69 ms ± 52.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Setup:

import itertools

import numpy as np
import pandas as pd
from numpy import nan

df1 = pd.DataFrame({
    'c1': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C', 'D', 'D', 'D'],
    'c2': ['P', 'T', 'Y', 'P', 'T', 'Y', 'P', 'T', 'Y', 'P', 'T', 'Y'],
    'v1': [4, 3, 2, 0, 2, 0, 1, 1, 1, 1, 2, 1],
    'v2': [1, 1, 0, 1, 2, 2, 2, 2, 1, 1, 0, 1]
})

df2 = pd.DataFrame({
    'c1': ['A', 'B', 'C', 'D'],
    'c2': [nan, nan, nan, nan],
    'v1': [9, 2, 3, 4],
    'v2': [2, 5, 5, 2]
})


def op_fn(df1, df2):
    result = []
    for i, row in df2.sort_values('v1').iterrows():
        result.append(row.to_frame().T)
        result.append(df1[df1['c1'].eq(row['c1'])].sort_values('v2'))
    return pd.concat(result, ignore_index=True)


def cat_ordered(df1, df2):
    # Establish ordering based on df2
    cat_type = pd.CategoricalDtype(df2.sort_values('v1')['c1'], ordered=True)
    return (
        # Add Indicator to each DataFrame
        pd.concat([df.assign(indicator=i) for (i, df) in enumerate([df1, df2])])
            # Set c1 to the categorical ordering from above
            .astype({'c1': cat_type})
            # Sort By Categorical, then df2 first then v2 within dfs
            .sort_values(['c1', 'indicator', 'v2'],
                         ascending=(True, False, True),
                         ignore_index=True)
            # Remove Indicator column
            .drop(columns='indicator')
    )


def groupby_fn(df1, df2):
    y = df1.assign(ind=df1['v1'])
    x = df2.groupby("c1").apply(
        lambda v: pd.concat(
            [y[y["c1"].eq(v["c1"].iat[0])], v.sort_values("v2")]
        )
    )
    x.loc[:, "ind"] = x.loc[:, "ind"].ffill()
    return x.sort_values("ind").drop(columns="ind").reset_index(drop=True)


def concat_itertools(df1, df2):
    out = pd.concat([df1.sort_values('v1'),
                     df2.sort_values('v2')],
                    ignore_index=True)
    return out.loc[itertools.chain.from_iterable(out.groupby('c1', sort=False)
                                                 .groups.values())]


def helpcol(df1, df2):
    cat_type = pd.CategoricalDtype(df2.sort_values('v1')['c1'], ordered=True)
    dfc = pd.concat([df1, df2])
    dfc["c2sort"] = dfc["c2"].notna()
    dfc["c1sort"] = dfc["c1"].astype(cat_type)
    return dfc.sort_values(["c1sort", "c2sort", "v2"], ignore_index=True).drop(
        ["c2sort", "c1sort"], axis=1
    )


def U11(df1, df2):
    df = pd.concat([df1, df2], ignore_index=True)
    return (
        df.reindex(
            df.sort_values('c1')
                .groupby('c1', as_index=False)['v1'].transform('min')
                .squeeze().sort_values().index
        ).reset_index(drop=True)
    )


def mysort_anky(df1, df2):
    d = dict(zip(df2['c1'], df2['v1'].rank()))
    o = pd.concat((df2, df1), keys=[1, 2], names=['Key'])

    return (o.assign(k=o['c1'].map(d)).sort_values(['k', 'Key', 'v2'])
                .loc[:, list(df1)])  # .reset_index(drop=True)


def mysort_two_anky(df1, df2):
    d = dict(zip(df2['c1'], df2['v1'].rank()))
    o = pd.concat((df2, df1), keys=[1, 2], names=['Key'])
    a = o.to_numpy()[np.lexsort((o['v2'], o.index.get_level_values('Key'),
                                 o['c1'].map(d)))]
    return pd.DataFrame(a, columns=df2.columns)
like image 30
Henry Ecker Avatar answered Sep 29 '22 17:09

Henry Ecker
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