Anyone knows how to find potential join between two pandas dataframe?
eg. I want to search for potential match between columns of these datasets
I have developed something like that
from tqdm import tqdm
import numpy as np
def _check_na(df, col, threshold=0.9):
return sum(df[col].isna()) > (1- threshold) * df.shape[0]
def _check_dtype(df1, df2, c1, c2):
try:
df1[c1] = df1[c1].astype(df2.dtypes[c2])
return True
except:
return False
def _fast_intersect(df1, df2, c1, c2, perc=0.2, maxrows=1000):
n = min(df1.shape[0], df2.shape[0])
c=1
if n>maxrows:
c = len(np.intersect1d(df1[c1].sample(n= maxrows, random_state=0).astype(str).values, df2[c2].astype(str)) )
if (c>0) or n<=maxrows:
return len(np.intersect1d(df1[c1].astype(str).values, df2[c2].astype(str).values))
return 0
def possible_join(df1, df2, threshold=0.8, verbose=0):
n = min(df1.shape[0], df2.shape[0])
for c1 in tqdm(df1.columns):
if _check_na(df1, c1):
if verbose>1:
print('WARN: too many na',c1)
continue
for c2 in df2.columns:
if not(_check_dtype(df1, df2, c1, c2)):
if verbose>1:
print('WARN: incompatible type',c1, df1.dtypes[c1],c2, df2.dtypes[c2])
continue
if _check_na(df2, c2):
if verbose>1:
print('WARN: too many na',c2)
continue
c = _fast_intersect(df1, df2, c1, c2)
print(c)
if (c>= threshold * n):
print ('** MATCH **:', c1,'-',c2,':', c, c/n, '%')
elif (c>= threshold * n /2):
print ('** LOW MATCH **:',c1,'-',c2,':', c, c/n, '%')
_check_dtype
check for potentila match between data types
_check_na
excludes columns with a lot of NaN
_fast_intersect
tries a fast join with only few data, if it passes look for full join.
but is is too slow!
test
d = {'col11': [1, 2], 'col21': ['3', '4']}
df1 = pd.DataFrame(data=d)
d = {'col12': [1, 3], 'col22': [3, 4]}
df2 = pd.DataFrame(data=d)
possible_join(df1, df2, threshold=0.6, verbose=2)
the result is
col21 and col22 with 100% full match
col11 and col12 with 50% low match
use this peace of code in _fast_intersect
:
first_df = df1.sample(n= int(n * perc), random_state=0)
second_df = df2
return pd.merge(first_df, second_df, how="inner", on="c2").shape[0]
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