<jupyter_start><jupyter_text>concatenation<jupyter_code>pd.concat([df1,df2,df3],axis=1)
left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                     'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                         'C': ['C0', 'C1', 'C2', 'C3'],
                        'D': ['D0', 'D1', 'D2', 'D3']}) 
left
right<jupyter_output><empty_output><jupyter_text>marging<jupyter_code>pd.merge(left,right,how='inner',on='key')
left1 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                     'key1': ['K0', 'K1', 'K2', 'K3'],
                     'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})

right1 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                      'key1': ['K0', 'K1', 'K2', 'K3'],
                         'C': ['C0', 'C1', 'C2', 'C3'],
                        'D': ['D0', 'D1', 'D2', 'D3']}) 
right1
left1
pd.merge(right1,left1,on=['key','key1'])<jupyter_output><empty_output><jupyter_text>joinning<jupyter_code>left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
                    'B': ['B0', 'B1', 'B2']},
                    index=['k0', 'k1', 'k2'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2'],
                    'D': ['D0', 'D1', 'D2']},
                    index=['k0', 'k2', 'k3'])
left
right
left.join(right)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.28842105263157897, 'fraction_numerical': 0.061052631578947365, 'mean_word_length': 2.324009324009324, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '344458', 'license': 'MIT', 'n_tokens_mistral': 628, 'n_tokens_neox': 612, 'n_words': 106, 'path': 'Pandas/panda_marging_joining_conca.ipynb', 'repo_name': 'DEKKAR-AHMED/data_Science_Learning', 'size': '1425'}