<jupyter_start><jupyter_text>Base dataframe<jupyter_code>df = pd.read_csv(items_fh)
df.head()<jupyter_output><empty_output><jupyter_text>$0 values<jupyter_code>df["0"].value_counts()<jupyter_output><empty_output><jupyter_text>$1 values<jupyter_code>df["1"].value_counts()<jupyter_output><empty_output><jupyter_text>$2 values<jupyter_code>df["2"].value_counts()<jupyter_output><empty_output><jupyter_text>$4 values<jupyter_code>df["4"].value_counts()
df["4"].value_counts().to_csv("./files/sub_$4.csv", header=True)<jupyter_output><empty_output><jupyter_text>$5 values<jupyter_code>df["5"].value_counts()
df["5"].value_counts().to_csv("./files/sub_$5.csv", header=True)<jupyter_output><empty_output><jupyter_text>$7 values<jupyter_code>df["7"].value_counts().to_csv("./files/sub_$7.csv", header=True)<jupyter_output><empty_output><jupyter_text>$A values<jupyter_code>df["A"].value_counts()<jupyter_output><empty_output><jupyter_text>$a values<jupyter_code>df["a"].value_counts()
df["a"].value_counts().to_csv("./files/sub_$a.csv", header=True)<jupyter_output><empty_output><jupyter_text>$B values<jupyter_code>df["B"].value_counts()
df["B"].value_counts().to_csv("./files/sub_$B.csv", header=True)<jupyter_output><empty_output><jupyter_text>$b values<jupyter_code>df["b"].value_counts()
df["b"].value_counts().to_csv("./files/sub_$b_.csv", header=True)<jupyter_output><empty_output><jupyter_text>$F values<jupyter_code>df["F"].value_counts()
df["F"].value_counts().to_csv("./files/sub_$F.csv", header=True)<jupyter_output><empty_output><jupyter_text>$f values<jupyter_code>df["f"].value_counts()
df["f"].value_counts().to_csv("./files/sub_$f_.csv", header=True)<jupyter_output><empty_output><jupyter_text>$H value<jupyter_code>df["H"].value_counts()<jupyter_output><empty_output><jupyter_text>$h values<jupyter_code>df["h"].value_counts()
df["h"].value_counts().to_csv("./files/sub_$h.csv", header=True)<jupyter_output><empty_output><jupyter_text>$I values<jupyter_code>df["I"].value_counts()
df["I"].value_counts().to_csv("./files/sub_$I.csv", header=True)<jupyter_output><empty_output><jupyter_text>$i values<jupyter_code>df["i"].value_counts()
df["i"].value_counts().to_csv("./files/sub_$i_.csv", header=True)<jupyter_output><empty_output><jupyter_text>$J values<jupyter_code>df["J"].value_counts()
df["J"].value_counts().to_csv("./files/sub_$J.csv", header=True)<jupyter_output><empty_output><jupyter_text>$j values<jupyter_code>df["j"].value_counts()
df["j"].value_counts().to_csv("./files/sub_j_.csv", header=True)<jupyter_output><empty_output><jupyter_text>$k values<jupyter_code>df["k"].value_counts()
df["k"].value_counts().to_csv("./files/sub_$k.csv", header=True)<jupyter_output><empty_output><jupyter_text>$m1 values<jupyter_code>df["m1"].value_counts()
df["m1"].value_counts().to_csv("./files/sub_$m1.csv", header=True)<jupyter_output><empty_output><jupyter_text>$m2 values<jupyter_code>df["m2"].value_counts()
df["m2"].value_counts().to_csv("./files/sub_$m2.csv", header=True)<jupyter_output><empty_output><jupyter_text>$P values<jupyter_code>df["P"].value_counts()
df["P"].value_counts().to_csv("./files/sub_$P.csv", header=True)<jupyter_output><empty_output><jupyter_text>$p values<jupyter_code>df["p"].value_counts()
df["p"].value_counts().to_csv("./files/sub_$p_.csv", header=True)<jupyter_output><empty_output><jupyter_text>$Q values<jupyter_code>df["Q"].value_counts()
df["Q"].value_counts().to_csv("./files/sub_$Q.csv", header=True)<jupyter_output><empty_output><jupyter_text>$R values<jupyter_code>df["R"].value_counts()
df["R"].value_counts().to_csv("./files/sub_$R.csv", header=True)<jupyter_output><empty_output><jupyter_text>$S values<jupyter_code>df["S"].value_counts()
df["S"].value_counts().to_csv("./files/sub_$S.csv", header=True)<jupyter_output><empty_output><jupyter_text>$w values<jupyter_code>df["w"].value_counts()
df["w"].value_counts().to_csv("./files/sub_$w.csv", header=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21779918864097364, 'fraction_numerical': 0.006338742393509128, 'mean_word_length': 15.787234042553191, 'pattern_counts': {'":': 0, '<': 113, '<?xml version=': 0, '>': 113, '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': '28', 'dir': 'jupyter-structured-clean-dedup', 'id': '581604', 'license': 'MIT', 'n_tokens_mistral': 1723, 'n_tokens_neox': 1672, 'n_words': 74, 'path': 'src/item-analysis.ipynb', 'repo_name': 'BookOps-CAT/CBH-migration', 'size': '3944'}