I have two separate data frames named df1 and df2 as shown below:
Scaffold Position Ref_Allele_Count Alt_Allele_Count Coverage_Depth Alt_Allele_Frequency
0 1 11 7 51 58 0.879310
1 1 16 20 95 115 0.826087
2 2 9 9 33 42 0.785714
3 2 12 86 51 137 0.372263
4 2 67 41 98 139 0.705036
5 3 8 0 0 0 0.000000
6 4 99 32 26 58 0.448276
7 4 101 100 24 124 0.193548
8 4 115 69 26 95 0.273684
9 5 6 40 57 97 0.587629
10 5 19 53 87 140 0.621429
Scaffold Position Ref_Allele_Count Alt_Allele_Count Coverage_Depth Alt_Allele_Frequency
0 1 11 7 64 71 0.901408
1 1 16 10 90 100 0.900000
2 2 9 79 86 165 0.521212
3 2 12 12 73 85 0.858824
4 2 67 54 96 150 0.640000
5 3 8 0 0 0 0.000000
6 4 99 86 28 114 0.245614
7 4 101 32 25 57 0.438596
8 4 115 97 16 113 0.141593
9 5 6 86 43 129 0.333333
10 5 19 59 27 86 0.313953
I have already found the sum values for df1 and df2 in Allele_Count and Coverage Depth but I need to divide the resulting Alt_Allele_Count and Coverage_Depth of both df's with one another to fine the total allele frequency(AF). I have tried dividing the two variable and got the error message : TypeError: float() argument must be a string or a number, not 'DataFrame' when I tried to convert them to floats and this table when I laft it as a df:
Alt_Allele_Count Coverage_Depth
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
6 NaN NaN
7 NaN NaN
8 NaN NaN
9 NaN NaN
10 NaN NaN
My code so far:
import csv
import pandas as pd
import numpy as np
df1 = pd.read_csv('C:/Users/Tom/Python_CW/file_pairA_1.csv')
df2 = pd.read_csv('C:/Users/Tom/Python_CW/file_pairA_2.csv')
print(df1)
print(df2)
Ref_Allele_Count = (df1[['Ref_Allele_Count']] df2[['Ref_Allele_Count']])
print(Ref_Allele_Count)
Alt_Allele_Count = (df1[['Alt_Allele_Count']] df2[['Alt_Allele_Count']])
print(Alt_Allele_Count)
Coverage_Depth = (df1[['Coverage_Depth']] df2[['Coverage_Depth']]).astype(float)
print(Coverage_Depth)
AF = Alt_Allele_Count / Coverage_Depth
print(AF)
CodePudding user response：
The error stems from the difference between a pandas series and a dataframe. Series are 1 dimensional structures like a singular column, while dataframes are 2d objects like tables. Series added together make a new series of values while dataframes added together make something a lot less usable.
Taking slices of a dataframe can either result in a series or dataframe object depending on how you do it:
df['column_name'] -> Series
df[['column_name', 'column_2']] -> Dataframe
So in the line:
Ref_Allele_Count = (df1[['Ref_Allele_Count']] df2[['Ref_Allele_Count']])
df1[['Ref_Allele_Count']] becomes a singular column dataframe rather than a series.
Ref_Allele_Count = (df1['Ref_Allele_Count'] df2['Ref_Allele_Count'])
Should return the correct result here. Same goes for the rest of the columns you're adding together.
CodePudding user response：
This can be fixed by only using once set of brackets '[]' while referring to a column in a pandas df, rather than 2.