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pythonnumpypandasdistribution
nahsivar
nahsivar

Reputation: 1099

Creating a matrix of joint number of hits from two columns using numpy/pandas

I have 2 large columns of data (some 1.5million values). They are structured as :

     col1 = [2,2,1,4,5,4,3,4,4,4,5,2,3,1,1 ..] etc.,
     col2 = [1,1,8,8,3,5,6,7,2,3,10.........] etc.,

I want to make a joint count matrix CountsMAT. col1 has values ranging from 1 to 5 and col2 has a range of 1 to 10.

     counts of [(1,2),(1,3),...(1,10),
                (2,1), (2,2),....(2,10),
                (3,1),......,(3,3)...(3,10),
                ...........................
                (5,1),(5,2),...(5,5).....(5,10)] are required ie.,

     a CountsMAT of size (5,10) 
     max(col1) = 5; max(col2) = 10 ;

I've implemented a version with defaultdict & for loop, but that takes a while. Am sure this could be more adeptly handled with Pandas directly and I'd appreciate an optimal implementation for it using numpy/pandas. many other similar queries point to 'groupby' of Pandas, but am not well-versed with it.

Getting the output in the matrix format is trickier also for my dict based implementation. but guess it will be easier with Pandas/numpy. Thanks!

Upvotes: 2

Views: 797

Answers (2)

Eelco Hoogendoorn
Eelco Hoogendoorn

Reputation: 10769

The numpy_indexed package (disclaimer: I am its author) has a function to solve this problem efficiently:

import numpy_indexed as npi
print(npi.count_table(col1, col2))

Upvotes: 1

wflynny
wflynny

Reputation: 18541

This might work (using numpy.histogram2d):

import numpy as np

col1 = np.random.random_integers(1, 5, 100)
col2 = np.random.random_integers(1, 10, 100)
bin1 = np.arange(0.5, 6.5, 1)
bin2 = np.arange(0.5, 11.5, 1)

mat = np.histogram2d(col1, col2, bins=[bin1, bin2])[0]
print mat.shape
print mat

which yields

(5, 10)
array([[ 4.,  2.,  0.,  6.,  2.,  2.,  1.,  2.,  1.,  2.],
       [ 3.,  3.,  3.,  0.,  3.,  0.,  1.,  4.,  1.,  0.],
       [ 4.,  2.,  1.,  1.,  3.,  2.,  5.,  2.,  2.,  2.],
       [ 1.,  1.,  3.,  2.,  3.,  1.,  4.,  4.,  0.,  0.],
       [ 0.,  2.,  1.,  4.,  3.,  1.,  3.,  2.,  0.,  1.]])

I haven't tested it using lists with lengths over a couple thousand, but I think it should scale OK for a few million values.

Edit:

Hi @nahsivar. I'm not too familiar with pandas (but I should probably be), so I spent a few minutes playing around. Here are several ways to get what you want (I think):

#instantiate dataframe
import pandas as pd
from random import choice
x_choice = 'ABCDE'
y_choice = 'ABCDEFGHIJ'
x, y = [], []
for i in range(100):
    x[i] = choice(x_choice)
    y[i] = choice(y_choice)

df = pd.DataFrame(data={'col1': x, 'col2': y})

# 1
df.pivot_table(rows='col1', cols='col2', aggfunc=len) 
# Use fill_value=0 to replace the NaNs with 0
# Output:
col2   A  B   C   D   E  F  G  H  I   J
col1                                   
A    NaN  1   3   1   2  2  2  1  4   2
B      1  1 NaN   3   5  1  2  3  1 NaN
C      4  1   2 NaN NaN  4  3  2  1   2
D      2  2   2   1   1  3  3  4  4   2
E      1  1   1 NaN   4  2  6  3  2   2

# 2
df.groupby('col2')['col1'].value_counts().unstack(level=0)
# Output:
col2   A  B   C   D   E  F  G  H  I   J
A    NaN  1   3   1   2  2  2  1  4   2
B      1  1 NaN   3   5  1  2  3  1 NaN
C      4  1   2 NaN NaN  4  3  2  1   2
D      2  2   2   1   1  3  3  4  4   2
E      1  1   1 NaN   4  2  6  3  2   2

# 3
pd.crosstab(df.col1, df.col2)
# Output:
col2  A  B  C  D  E  F  G  H  I  J
col1                              
A     0  1  3  1  2  2  2  1  4  2
B     1  1  0  3  5  1  2  3  1  0
C     4  1  2  0  0  4  3  2  1  2
D     2  2  2  1  1  3  3  4  4  2
E     1  1  1  0  4  2  6  3  2  2

Upvotes: 5

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