<jupyter_start><jupyter_text>Merge/Joinpd.merge(left=df1, right=df2, on= 'colname', how= 'type of merge)- we use the common column (on=) in both dfs to join/merge them    * inner(how=): returns rows that exist in both dfs    * outer: all the values will be joined but if we don't have match then it passes NaN    * right: produce rows from both dfs where there is a match and if not passes NaN / it first pass the rows that are similar in both and then only add the rows from right df      * left: does exactly the opposite of right<jupyter_code>left_frame = pd.DataFrame({'key': range(5),
                           'left_value': ['a', 'b', 'c', 'd', 'e']})
right_frame = pd.DataFrame({'key': range(2, 7),
                           'right_value': ['f', 'g', 'h', 'i', 'j']})
print(left_frame)
print('\n')
print(right_frame)
pd.merge(left_frame, right_frame, on='key', how='inner') # only the rows that are identical in both dfs
pd.merge(left_frame, right_frame, on='key', how='right')
#keep the rows that are identical in left and right then stack the rest from right df and passes NaN for values
# that have not any identical value in the left here 5 and 6 keys
pd.merge(left_frame, right_frame, on='key', how='left') # opposite of right
pd.merge(left_frame, right_frame, on='key', how='outer') # stacl all the rows and passes NaN for non-identical 
# values<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12149532710280374, 'fraction_numerical': 0.0050323508267433505, 'mean_word_length': 4.043478260869565, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '518834', 'license': 'MIT', 'n_tokens_mistral': 437, 'n_tokens_neox': 429, 'n_words': 182, 'path': 'Intro data cleansing/titanic-200929-182639/Data Assembly.ipynb', 'repo_name': 'behzad1195/Data-Prepration-in-Python', 'size': '1391'}