CODEWITHSUNDEEP
python-3.xpandasjoindataframe
Edamame
Edamame

Reputation: 25366

Pandas: Join multiple data frame on the same keys

I need to join 5 data frames using the same key. I created several temporary data frame while doing the join. The code below works fine, but I am wondering is there a more elegant way to achieve this goal? Thanks! 

df1 = pd.read_pickle('df1.pkl')
df2 = pd.read_pickle('df2.pkl')
df3 = pd.read_pickle('df3.pkl')
df4 = pd.read_pickle('df4.pkl')
df5 = pd.read_pickle('df5.pkl')

tmp_1 = pd.merge(df1, df2,  how ='outer', on = ['id','week'])
tmp_2 = pd.merge(tmp_1, df3,  how ='outer', on = ['id','week'])
tmp_3 = pd.merge(tmp_2, df4,  how ='outer', on = ['id','week'])
result_df = pd.merge(tmp_3, df5,  how ='outer', on = ['id','week'])

Upvotes: 2

Views: 2494

Answers (1)

piRSquared
piRSquared

Reputation: 294338

Use pd.concat after setting the index

dfs = [df1, df2, df3, df4, df5]
cols = ['id', 'weedk']
df = pd.concat([d.set_index(cols) for d in dfs], axis=1).reset_index()

Include file reading

from glob import glob

def rp(f):
    return pd.read_pickle(f).set_index(['id', 'week'])

df = pd.concat([rp(f) for f in glob('df[1-5].pkl')], axis=1).reset_index()

Upvotes: 3

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