How to split a 74 rows and 3234 columns dataframe on columns in python based on condition

Question

Say, I have a data frame of dimension (74, 3234), 74 rows, and 3234 columns. I have a function to run a correlation analysis. However, when I give this data frame as it is, it is taking forever to print the results. Now I would like to split the data frame into multiple chunks. And use the chucks in the function.

The data frame has 20,000 columns with the column names containing string _PC and 15000 columns with string _lncRNAs .

The condition which needs to follow is, I what I need to split the data frame into multiple smaller dataframe, which contain both columns with _PC and _lncRNAs column names. For example df1 must contain 500 columns with _PC and 500 columns with _lncRNAs strings.

I envision having multiple data frames. For example always 74 rows, but using consecutive column. for instance, 1-500, 501-1000, 10001 -1500, 1501-2000, so on until the last column

 `df1.shape`
(74, 500)
df2.shape
(74, 500)

... so on

one example

df1.head()
sam   END_PC  END2_PC END3_lncRNAs END4_lncRNAs
SAP1    50.9   30.4   49.0          50
SAP2      6    8.9     12.4 39.8   345.9888

Then, I need to use each split data frame on the following function.

def correlation_analysis(lncRNA_PC_T):
    """
    Function for correlation analysis
    """
    correlations = pd.DataFrame()
    for PC in [column for column in lncRNA_PC_T.columns if '_PC' in column]: 
        for lncRNA in [column for column in lncRNA_PC_T.columns if '_lncRNAs' in column]:
                    correlations = correlations.append(pd.Series(pearsonr(lncRNA_PC_T[PC],lncRNA_PC_T[lncRNA]),index=['PCC', 'p-value'],name=PC + '_' +lncRNA))
    correlations.reset_index(inplace=True)
    correlations.rename(columns={0:'name'},inplace=True)
    correlations['PC']         = correlations['index'].apply(lambda x:x.split('PC')[0])
    correlations['lncRNAs']    = correlations['index'].apply(lambda x:x.split('PC')[1])
    correlations['lncRNAs']    = correlations['lncRNAs'].apply(lambda x:x.split('_')[1])
    correlations['PC']         = correlations.PC.str.strip('_')
    correlations.drop('index',axis=1,inplace=True)
    correlations               = correlations.reindex(columns=['PC','lncRNAs','PCC','p-value']) 
              
    return(correlations)

For each, data frame output should look like this,

              gene          PCC   p-value
END_PC_END3_lncRNAs  -0.042027   0.722192
END2_PC_END3_lncRNAs  -0.017090   0.885088
END_PC_END4_lncRNAs    0.001417    0.990441
END2_PC_END3_lncRNAs  -0.041592   0.724954

I know one can split based on rows like this,

n = 200000  #chunk row size
list_df = [df[i:i+n] for i in range(0,df.shape[0],n)]

I want something like this based on columns. Any suggestions or help is much appreciated. Thanks

Answer 1

How about df.iloc ?

And use df.shape[1] for the number of columns:

list_df = [df.iloc[:, i:i+n] for i in range(0, df.shape[1], n)]

Ref: How to take column-slices of dataframe in pandas

Answer 2

It's just like wrote Basil but using pandas.DataFrame.iloc

I do not know what are the columns labels. So in order to make this independent of the index or column labels, is better to use:

list_df = [df.iloc[:,i:i+n] for i in range(0, df.shape[1], n)]

See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html

Answer 3

This is what I tried to see how long it takes to evaluate the correlation between the rows and the columns of the dataframe ( df ). It took under 50 milliseconds for rows-correlation and under 2 seconds for columns-correlation .

• Rows-correlation output-shape: (74x74)
• Columns-correlation output-shape: (3000x3000)

Correlation b/w some target-columns and all-columns

# Define target columns
target_cols = ['C0', 'C12', 'C100']

# Extract correlation-result for target-columns
corr_result = df_corr[target_cols]
print(corr_result)

Output :

             C0       C12      C100
C0     1.000000 -0.031120 -0.221829
C1     0.064772 -0.130507 -0.086164
C2     0.077853 -0.116949  0.003468
C3     0.070557 -0.013551  0.007093
C4     0.165782 -0.058755 -0.175888
...         ...       ...       ...
C2995 -0.097033 -0.014391  0.018961
C2996  0.099591  0.017187 -0.016138
C2997 -0.126288  0.145150 -0.089306
C2998  0.033484  0.054106 -0.006594
C2999 -0.154657  0.020002 -0.104889

Dummy Data

import numpy as np
import pandas as pd

## Create Dummy Data
a = np.random.rand(74, 3000)
print(f'a.shape: {a.shape}')

## Create Dataframe
index = [f'R{i}' for i in range(a.shape[0])]
columns = [f'C{i}' for i in range(a.shape[1])]
df = pd.DataFrame(a, columns=columns, index=index)
df.shape # (74, 3000)

Evaluate Correlation

I did the following in a jupyter notebook

## Correlation between Rows of dfp
%%time
df.T.corr()
#CPU times: user 39.5 ms, sys: 1.09 ms, total: 40.6 ms
#Wall time: 41.3 ms

## Correlation between Columns of dfp
%%time
df.corr()
# CPU times: user 1.64 s, sys: 34.6 ms, total: 1.67 s
# Wall time: 1.67 s

Output: df.corr()

Since, the shape of the dataframe was (74, 3000) , df.corr() yields a dataframe of shape (3000, 3000) .

             C0        C1        C2  ...     C2997     C2998     C2999
C0     1.000000  0.064772  0.077853  ... -0.126288  0.033484 -0.154657
C1     0.064772  1.000000  0.031059  ...  0.064317  0.095075 -0.100423
C2     0.077853  0.031059  1.000000  ... -0.123791 -0.034085  0.052334
C3     0.070557  0.229482  0.047476  ...  0.043630 -0.055772  0.037123
C4     0.165782  0.189635 -0.009193  ... -0.123917  0.097660  0.074777
...         ...       ...       ...  ...       ...       ...       ...
C2995 -0.097033 -0.126214  0.051592  ...  0.008921 -0.004141  0.221091
C2996  0.099591  0.030975 -0.081584  ...  0.186931  0.084529  0.063596
C2997 -0.126288  0.064317 -0.123791  ...  1.000000  0.061555  0.024695
C2998  0.033484  0.095075 -0.034085  ...  0.061555  1.000000  0.195013
C2999 -0.154657 -0.100423  0.052334  ...  0.024695  0.195013  1.000000

Answer 4

If you want the correlation between each column with _PC versus the columns with _lncRNAs string, you could try something like this:

df_pc=df.filter(like='_PC')
df_lncRNAs=df.filter(like='_lncRNAs')
pd.concat([df_pc, df_lncRNAs], axis=1, keys=['df1', 'df2']).corr().loc['df2', 'df1']

Example:

import pandas as pd
df = pd.DataFrame({"a_pc":[1,2,3,4,5,6],
                  "b_pc":[3,210,12,412,512,61]
                   ,"c_pc": [1,2,3,4,5,6]
                 ,"d_lncRNAs": [3,210,12,412,512,61]
                 ,"d1_lncRNAs": [3,210,12,412,512,61]})

df_pc=df.filter(like='_pc')
df_lncRNAs=df.filter(like='_lncRNAs')
correlation=pd.concat([df_pc, df_lncRNAs], axis=1, keys=['df1', 'df2']).corr().loc['df2', 'df1']
correlation

Output:

df
   a_pc  b_pc  c_pc  d_lncRNAs  d1_lncRNAs
0     1     3     1          3           3
1     2   210     2        210         210
2     3    12     3         12          12
3     4   412     4        412         412
4     5   512     5        512         512
5     6    61     6         61          61

df_pc
   a_pc  b_pc  c_pc
0     1     3     1
1     2   210     2
2     3    12     3
3     4   412     4
4     5   512     5
5     6    61     6

df_lncRNAs 
   d_lncRNAs  d1_lncRNAs
0          3           3
1        210         210
2         12          12
3        412         412
4        512         512
5         61          61

correlation
                a_pc  b_pc      c_pc
d_lncRNAs   0.392799   1.0  0.392799
d1_lncRNAs  0.392799   1.0  0.392799