<jupyter_start><jupyter_text>The script would read "source_file" as source of "Area" and replace all "Area" in "to_replace" at corresponding "First Scan" values. The output filename and path are defined at "output".<jupyter_code>import pandas as pd
source_file = r"C:\Users\localadmin\Downloads\20190418_17_Schulz_Ruby_IgG_mz600_ETH1_MSnSpectrumInfo.xlsx"
to_replace = r"C:\Users\localadmin\Downloads\ETH1.xlsx"
output = r"C:\Users\localadmin\Downloads\ETH1_updated.xlsx"
source_df = pd.read_excel(source_file, index_col=15)
to_replace_df = pd.read_excel(to_replace)<jupyter_output><empty_output><jupyter_text>Iterate through "to_replace_df" and replace "Area" with value at the corresponding "First Scan" from "source_file"<jupyter_code>for i, r in to_replace_df.iterrows():
    to_replace_df.at[i, "Area"] = source_df.loc[r["First Scan"]]["Area"]
to_replace_df.to_excel(output, index=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.113882863340564, 'fraction_numerical': 0.019522776572668113, 'mean_word_length': 8.23, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '648587', 'license': 'MIT', 'n_tokens_mistral': 360, 'n_tokens_neox': 340, 'n_words': 67, 'path': 'MS/replacing area.ipynb', 'repo_name': 'bschulzlab/data-processing', 'size': '922'}