用来数出df里某列 tag counts数量, 然后可视化的代码:
def safe_split_tag_str(tag_str, separator=","):
"""
Splits a tag string into a list of non-empty, whitespace-stripped tag strings.
"""
if not tag_str:
return []
# Split the tag string using the separator, strip whitespace, and filter out empty strings
tags = [tag.strip() for tag in tag_str.split(separator) if tag.strip()]
return tags
def visualize(df, columns, range=(0, 100)):
"""
Visualizes the distribution of a specified column in the DataFrame.
"""
start, end = range
alpha = 1 / len(columns)
plt.figure(figsize=(14, 7))
for column in columns:
plt.hist(df[column], bins=np.arange(start, end, 1), alpha=alpha)
plt.legend(columns)
plt.title(f'Distribution of {len(columns)} columns: {", ".join(columns)}')
plt.xlabel(columns)
plt.ylabel('Frequency')
plt.show()
def add_tag_counts(df):
df['adjusted_tag_count'] = df['general_tags_all'].apply(lambda x: len(x.split(',')))
df['wd_tag_count'] = df['wd_general'].apply(lambda x: len(safe_split_tag_str(x)))
df['mld_tag_count'] = df['mld_tags'].apply(lambda x: len(safe_split_tag_str(x)))
df["pixiv_tag_count"] = df["tags"].apply(lambda x: len(safe_split_tag_str(x)))
df["danbooru_tag_count"] = df["tag_string_general"].apply(lambda x: len(safe_split_tag_str(x)))
return df
vis_cols = ["adjusted_tag_count", "danbooru_tag_count"]
df_danbooru = df_sample[df_sample["tag_string_general"].notna()].copy()
df_danbooru = add_tag_counts(df_danbooru)
visualize(df_danbooru, vis_cols, range=(0, 100))
# vis_cols = ["adjusted_tag_count", "wd_tag_count", "mld_tag_count", "pixiv_tag_count", "danbooru_tag_count"]
# df_sample = add_tag_counts(df_sample)
# visualize(df_sample, vis_cols, range=(0, 100))图:
