CODEWITHSUNDEEP
pythonpandasdictionarydataframe
The Nightman
The Nightman

Reputation: 5759

Comparing data between pandas DataFrame and dictionary

I'm trying to make some comparisons between a dictionary and a pandas DataFrame.

The DataFrame looks like this:

      A      B     C
0    'a'    'x'    0
1    'b'    'y'    1
2    'c'    'z'    4

The dictionary looks like this:

{
'a-x': [1],
'b-y': [2],
'c-z': [3]
}

The goal is to use the dictionary keys to identify matching rows in the DataFrame (key 'a-x' matches index 0 of column A and column B) and then identify the DataFrame data in column C that is greater than the associated value of the dictionary.

So:

key 'a-x' matches index 0 of column A and column B, but value of 0 in C is less than 1 > exclude
key 'b-y' matches index 1 of column A and column B, but value of 1 in C is less than 2 > exclude
key 'c-z' matches index 2 of column A and column B, and value of 4 in C is greater than 3 > include

The filtered DataFrame would then only include the entry at index 2 and look like this:

      A      B     C
2    'c'    'z'    4

In case there are some details that matter this is a sample of my actual data

DataFrame:

    Chrom   Loc         WT  Var Change  ConvChange  AO  DP  VAF IntEx    Gene   Upstream    Downstream  Individual  ID
0   chr1    115227854   T   A   T>A     T>A         2   17224   0.0116117   TIII    TIIIa   NaN NaN 1   113.fastq/onlyProbedRegions.vcf

Dictionary:

rates =
{
    'chr1-115227854-T-A': [0.0032073647185113397]
}

Code:

return df[(df.Chrom+'-'+str(df.Loc)+'-'+df.WT+'-'+df.Var).map(pd.Series(rates).str[0])<df.VAF]

Upvotes: 0

Views: 2698

Answers (1)

BENY
BENY

Reputation: 323266

Create the pd.Series then using map create Boolean index

d={
'a-x': [1],
'b-y': [2],
'c-z': [3]
}
pd.Series(d)
Out[335]:
a-x    [1]
b-y    [2]
c-z    [3]
dtype: object


df[(df.A+'-'+df.B).map(pd.Series(d).str[0])<df.C]
Out[340]: 
   A  B  C
2  c  z  4

Upvotes: 1

Related Questions