<jupyter_start><jupyter_text>Base notebook- This is a notebook template  Imports  Standard library imports<jupyter_code>import sys
sys.path.append("../")

import os<jupyter_output><empty_output><jupyter_text>Third party imports<jupyter_code>from moviepy.editor import VideoFileClip
import pandas as pd<jupyter_output><empty_output><jupyter_text>Local application imports<jupyter_code>%load_ext autoreload
%autoreload 2

from pkg_dir.config import *
from pkg_dir.src.utils import *
from pkg_dir.src.functions import *
from pkg_dir.src.parameters import *<jupyter_output><empty_output><jupyter_text>Reading all USMLE videos and storing data as tuples<jupyter_code>videos_entries = videos_entries_list()
videos_entries<jupyter_output><empty_output><jupyter_text>Converting the video tuples into a dataframe<jupyter_code>df_vids = video_data_to_df(videos_entries, True)
df_vids
df_vids[df_vids["video"].str.contains("Gonad Dev")]<jupyter_output><empty_output><jupyter_text>*Notes*  Calculating duration for one video<jupyter_code>duration_test_path
video_test = VideoFileClip(duration_test_path)
video_test_dur = video_test.duration
video_test_dur
video_test_dur/60<jupyter_output><empty_output><jupyter_text>Creating dataframes from nested dictionaries<jupyter_code>test_dict_dir<jupyter_output><empty_output><jupyter_text>df_rows = []for topic in test_dict_dir:        for subtopic in test_dict_dir[topic]:                for class_name in test_dict_dir[topic][subtopic]:                        df_rows.append((topic, subtopic, class_name))        df_rows<jupyter_code>df_rows = [
    (topic, subtopic, class_name)
    for topic in test_dict_dir
    for subtopic in test_dict_dir[topic]
    for class_name in test_dict_dir[topic][subtopic]
]

df_rows
pd.DataFrame(df_rows, columns=["A", "B", "C"])<jupyter_output><empty_output><jupyter_text>Notes to fix importing problem in notion<jupyter_code>dfx = pd.read_csv("/Users/rp_mbp/Downloads/time_awareness_db.csv")
dfx["creation"] = pd.to_datetime(dfx["Base de agradecimientos"], errors="coerce", format="%y%m%d")
dfx.to_csv("notion_awarenes.csv")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0999057492931197, 'fraction_numerical': 0.001413760603204524, 'mean_word_length': 7.700819672131147, 'pattern_counts': {'":': 0, '<': 37, '<?xml version=': 0, '>': 37, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '9', 'dir': 'jupyter-structured-clean-dedup', 'id': '413766', 'license': 'MIT', 'n_tokens_mistral': 777, 'n_tokens_neox': 753, 'n_words': 128, 'path': 'notebooks/learning_time_nb.ipynb', 'repo_name': 'robperch/tash_usmle', 'size': '2122'}