diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 80d7fab..510b0cc 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,7 +1,7 @@
 repos:
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: "v0.0.254"
+    rev: "v0.3.7"
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
diff --git a/app/analyse_app_OLD.py b/app/analyse_app_OLD.py
index 0606418..dce7f36 100644
--- a/app/analyse_app_OLD.py
+++ b/app/analyse_app_OLD.py
@@ -11,8 +11,11 @@
 st.divider()
 st.header("Fait par la dream team _Analyse de données_")
 st.write(
-    "Cette application analyse les données du PoC. On peut se faire plaisir en y ajoutant tous les graphiques nécessaires. "
-    + "Le code est à nettoyer pour une meilleure maintenance ;-) "
+    (
+        "Cette application analyse les données du PoC. On peut se faire plaisir"
+        " en y ajoutant tous les graphiques nécessaires. "
+        "Le code est à nettoyer pour une meilleure maintenance ;-) "
+    ),
 )
 
 
@@ -28,8 +31,9 @@
 # XXX est un nombre
 # Et Supprimer les lignes où toutes les valeurs sont NaN
 df = data[~data["TITRE"].str.contains(r"Contenu \d+", na=False)].dropna(how="all")
-df
-# ne conserver qu'une ligne sur 4  (ce qui revient à supprimer les informations des personnages 2, 3, 4 quand ils existent)
+
+# ne conserver qu'une ligne sur 4  (ce qui revient à supprimer les informations
+# des personnages 2, 3, 4 quand ils existent)
 df_truncated = df.iloc[::4]
 # Nettoyage du data set
 
@@ -39,10 +43,9 @@
 ### Convertir les types de données correctement ici
 # Convertir les années en entier
 annee = "ANNEE"
-df_truncated[annee] = (
-    pd.to_numeric(df_truncated[annee], errors="coerce").fillna(0).astype(int)
-)
-# Trouver les titres qui apparaissent plus de 4 fois dans la colonne "TITRE" (car chaque titre a 4 lignes, une pour chaque personnage)
+df_truncated[annee] = pd.to_numeric(df_truncated[annee], errors="coerce").fillna(0).astype(int)
+# Trouver les titres qui apparaissent plus de 4 fois dans la colonne "TITRE"
+# (car chaque titre a 4 lignes, une pour chaque personnage)
 titles_more_than_once = df_truncated["TITRE"].value_counts()
 titles_more_than_once = titles_more_than_once[titles_more_than_once > 1]
 
diff --git a/app/odi_streamlit.py b/app/odi_streamlit.py
index 9819bd7..6efc676 100644
--- a/app/odi_streamlit.py
+++ b/app/odi_streamlit.py
@@ -3,53 +3,37 @@
 Here's our first attempt at using data to create a table:
 """
 
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-
-
-# O.Importation des librairies nécessaires pour le script
-# Core Pkgs - Web application
-import streamlit as st
-from streamlit_option_menu import option_menu
 
-# Other Pkgs
-from PIL import Image
-import os
-import random
+# Export fichier
 
 # Datavisualisation
-import altair as alt
-import plotly.graph_objects as go
-import plotly.express as px
-import matplotlib.pyplot as plt
-from plotly.subplots import make_subplots
 
 import pandas as pd
-import numpy as np
-
-import time
-import re
+import plotly.express as px
 
-import json
+# O.Importation des librairies nécessaires pour le script
+# Core Pkgs - Web application
+import streamlit as st
 
-# Export fichier
-import pickle
+# Other Pkgs
 
-# 3.Setup de l'application Streamlit  - Streamlit webpage properties / set up the app with wide view preset and a title
+# 3.Setup de l'application Streamlit  - Streamlit webpage properties / set up the app
+# with wide view preset and a title
 st.set_page_config(
-    page_title="Observatoire des Imaginaires", page_icon="herb", layout="wide"
+    page_title="Observatoire des Imaginaires",
+    page_icon="herb",
+    layout="wide",
 )
 
 
 @st.cache_data  # 👈 Add the caching decorator
-def load_data(file):
+def load_data(file: str) -> pd.DataFrame:
     df = pd.read_csv(file, skiprows=1)
     return df
 
 
 # Load the data
-file_path = "../data/AnalyseReponsesTreatedData.csv"  #'../data/Analyse réponses.xlsx - Treated data.csv'
+file_path = "../data/AnalyseReponsesTreatedData.csv"  #'../data/Analyse réponses.xlsx - Treated data.csv'  # noqa: E501
 # ne pas lire la première ligne
 data = load_data(file_path)
 
@@ -74,8 +58,11 @@ def load_data(file):
 
     st.title("Fait par la dream team _Analyse de données_")
     st.write(
-        "Cette application analyse les données du PoC. On peut se faire plaisir en y ajoutant tous les graphiques nécessaires. "
-        + "Le code est à nettoyer pour une meilleure maintenance ;-) "
+        (
+            "Cette application analyse les données du PoC. On peut se faire plaisir"
+            " en y ajoutant tous les graphiques nécessaires. "
+            "Le code est à nettoyer pour une meilleure maintenance ;-) "
+        ),
     )
 
 
@@ -104,7 +91,8 @@ def load_data(file):
 # Et Supprimer les lignes où toutes les valeurs sont NaN
 df = data[~data["TITRE"].str.contains(r"Contenu \d+", na=False)].dropna(how="all")
 
-# ne conserver qu'une ligne sur 4  (ce qui revient à supprimer les informations des personnages 2, 3, 4 quand ils existent)
+# ne conserver qu'une ligne sur 4  (ce qui revient à supprimer
+# les informations des personnages 2, 3, 4 quand ils existent)
 df_truncated = df.iloc[::4]
 
 # Nettoyage du data set
@@ -154,10 +142,11 @@ def load_data(file):
 
     st.write()
     st.write(
-        f":blue[{round(100*len(set(df_truncated[df_truncated.TYPE == 'FILM']['TITRE']))/len(set(df_truncated['TITRE'])),2)}%] des contenus renseignés sont des films vs :blue[{round(100*len(set(df_truncated[df_truncated.TYPE == 'SÉRIE']['TITRE']))/len(set(df_truncated['TITRE'])),2)}%] des séries."
+        f":blue[{round(100*len(set(df_truncated[df_truncated.TYPE == 'FILM']['TITRE']))/len(set(df_truncated['TITRE'])),2)}%] des contenus renseignés sont des films vs :blue[{round(100*len(set(df_truncated[df_truncated.TYPE == 'SÉRIE']['TITRE']))/len(set(df_truncated['TITRE'])),2)}%] des séries.",  # noqa: E501
     )
 
-# Trouver les titres qui apparaissent plus de 4 fois dans la colonne "TITRE" (car chaque titre a 4 lignes, une pour chaque personnage)
+# Trouver les titres qui apparaissent plus de 4 fois dans la colonne "TITRE"
+# (car chaque titre a 4 lignes, une pour chaque personnage)
 
 
 titles_more_than_once = (
@@ -172,21 +161,29 @@ def load_data(file):
 with st.container(border=True):
     st.header("Films les plus fréquents")
     col_freq_film_select, col_freq_film_vide, col_freq_film_graph = st.columns(
-        [2, 0.5, 5]
+        [2, 0.5, 5],
     )
     with col_freq_film_select:
         type_choice = st.selectbox(
-            "Choisir un type", titles_more_than_once["TYPE"].unique(), index=None
+            "Choisir un type",
+            titles_more_than_once["TYPE"].unique(),
+            index=None,
         )
     with col_freq_film_graph:
         if type_choice == "FILM":
             t = titles_more_than_once.loc[
                 titles_more_than_once["TYPE"] == "FILM"
-            ].sort_values(by="compte", ascending=True)
+            ].sort_values(
+                by="compte",
+                ascending=True,
+            )
         elif type_choice == "SÉRIE":
             t = titles_more_than_once.loc[
                 titles_more_than_once["TYPE"] == "SÉRIE"
-            ].sort_values(by="compte", ascending=True)
+            ].sort_values(
+                by="compte",
+                ascending=True,
+            )
         else:
             t = titles_more_than_once.sort_values(by="compte", ascending=True)
 
@@ -210,20 +207,24 @@ def load_data(file):
 
         date_min = str(df_truncated.ANNEE.min())
         date_max = str(df_truncated.ANNEE.max())
-        date_pareto = date_group_df[date_group_df["periode_percent"] <= 80][
-            "ANNEE"
-        ].min()
+        date_pareto = (
+            date_group_df[date_group_df["periode_percent"] <= 80]["ANNEE"].min()  # noqa: PLR2004
+        )
         date_value_pareto = int(
             round(
-                date_group_df[date_group_df["periode_percent"] <= 80][
+                date_group_df[date_group_df["periode_percent"] <= 80][  # noqa: PLR2004
                     "periode_percent"
                 ].max(),
                 0,
-            )
+            ),
         )
 
         st.markdown(
-            f"Les contenus datent d`une période qui s`étend de {date_min} à {date_max}. {date_value_pareto}% des contenus sont postérieurs à {date_pareto}."
+            (
+                f"Les contenus datent d`une période qui s`étend de {date_min}"
+                f" à {date_max}. {date_value_pareto}% des contenus sont postérieurs à"
+                f" {date_pareto}."
+            ),
         )
 
         st.bar_chart(date_group_df, x="ANNEE", y="nb_titre")
@@ -246,25 +247,27 @@ def load_data(file):
             0,
         )
         country_group_df["country_percent"] = round(
-            100 * (country_group_df.nb_titre / country_group_df.nb_titre.sum()), 2
+            100 * (country_group_df.nb_titre / country_group_df.nb_titre.sum()),
+            2,
         )
 
         country_value_pareto = int(
             round(
-                country_group_df[country_group_df["country_percent"] >= 10][
+                country_group_df[country_group_df["country_percent"] >= 10][  # noqa: PLR2004
                     "country_percent"
                 ].sum(),
                 2,
-            )
+            ),
         )
         country_group_df_pareto = country_group_df[
-            country_group_df["country_percent"] >= 10
+            country_group_df["country_percent"] >= 10  # noqa: PLR2004
         ][["pays_rework", "country_percent"]].sort_values(
-            "country_percent", ascending=False
+            "country_percent",
+            ascending=False,
         )
 
         st.write(
-            f"A **:blue[{country_value_pareto}%]**, les 2 principaux pays dont les contenus sont les plus visionnés sont : {country_group_df.nlargest(2,'country_percent').reset_index(drop=True)['pays_rework'][0].capitalize()} ({country_group_df.nlargest(2,'country_percent').reset_index(drop=True)['country_percent'][0]}%) et {country_group_df.nlargest(2,'country_percent').reset_index(drop=True)['pays_rework'][1].capitalize()} ({country_group_df.nlargest(2,'country_percent').reset_index(drop=True)['country_percent'][1]}%)."
+            f"A **:blue[{country_value_pareto}%]**, les 2 principaux pays dont les contenus sont les plus visionnés sont : {country_group_df.nlargest(2,'country_percent').reset_index(drop=True)['pays_rework'][0].capitalize()} ({country_group_df.nlargest(2,'country_percent').reset_index(drop=True)['country_percent'][0]}%) et {country_group_df.nlargest(2,'country_percent').reset_index(drop=True)['pays_rework'][1].capitalize()} ({country_group_df.nlargest(2,'country_percent').reset_index(drop=True)['country_percent'][1]}%).",  # noqa: E501
         )
 
         fig_type = px.bar(
@@ -303,11 +306,15 @@ def load_data(file):
         percent_canal_visionne2 = round(canal_group_df.canal_percent.nlargest(2)[1], 2)
 
         st.markdown(
-            f"Les contenus sont visionnés principalement sur :blue[{canal_visionne1.capitalize()}] (:blue[{percent_canal_visionne1}%]) et :blue[{canal_visionne2.capitalize()}] (:blue[{percent_canal_visionne2}%]).\n\n La majorité des contenus visionnés sur :blue[{canal_visionne1.capitalize()}] ont pour pays d'origine :blue[{canal_country_group_df[canal_country_group_df['CANAL']==canal_visionne1].nlargest(1,'nb_titre').reset_index()['pays_rework'][0]}] (:blue[%]), alors que la majorité des contenus français sont visionnés xxx (xxx%).\n\n :blue[{round(canal_group_df.loc['Autre','canal_percent'],2)}%] des contenus sont visionnés sur un canal `Autre` que la liste proposée (cf ci-contre)"
+            f"Les contenus sont visionnés principalement sur :blue[{canal_visionne1.capitalize()}] (:blue[{percent_canal_visionne1}%]) et :blue[{canal_visionne2.capitalize()}] (:blue[{percent_canal_visionne2}%]).\n\n La majorité des contenus visionnés sur :blue[{canal_visionne1.capitalize()}] ont pour pays d'origine :blue[{canal_country_group_df[canal_country_group_df['CANAL']==canal_visionne1].nlargest(1,'nb_titre').reset_index()['pays_rework'][0]}] (:blue[%]), alors que la majorité des contenus français sont visionnés xxx (xxx%).\n\n :blue[{round(canal_group_df.loc['Autre','canal_percent'],2)}%] des contenus sont visionnés sur un canal `Autre` que la liste proposée (cf ci-contre)",  # noqa: E501
         )
 
-        # Les contenus sont visionnés principalement sur Netflix (29.91%) ou dans une salle de cinéma (28.97%). La majorité des contenus américains sont visionnés sur Netflix (40.48% des contenus US), alors que la majorité des contenus français sont visionnés au cinéma (44.19%).
-    # 23.36% des contenus sont visionnés sur un canal `autre` que la liste proposée (cf ci-dessous)
+        # Les contenus sont visionnés principalement sur Netflix (29.91%) ou dans
+        # une salle de cinéma (28.97%). La majorité des contenus américains sont
+        # visionnés sur Netflix (40.48% des contenus US), alors que la majorité
+        # des contenus français sont visionnés au cinéma (44.19%).
+    # 23.36% des contenus sont visionnés sur un canal `autre`
+    # que la liste proposée (cf ci-dessous)
 
     with col_table_canal:
         st.markdown(set(canal_group_df.reset_index().CANAL))
@@ -320,49 +327,28 @@ def load_data(file):
 st.dataframe(
     df_truncated[["TITRE", "TRAITEMENT DU RECIT", "TYPE DE MONDE"]]
     .groupby(["TRAITEMENT DU RECIT", "TYPE DE MONDE"])
-    .count()
+    .count(),
 )
 
-# 				st.write(f"Parmi les ouvrages parus et écrits par des autrices, **:blue[{int(round(st.session_state['roman_francais'][st.session_state['roman_francais']['Genre']==selection]['Nb ouvrages'].sum()/len(liste_ean)*100,0))}%]** sont des romans français, **:blue[{int(round(st.session_state['roman_etranger'][st.session_state['roman_etranger']['Genre']==selection]['Nb ouvrages'].sum()/len(liste_ean)*100,0))}%]** des romans étrangers et **:blue[{int(round(st.session_state['essais'][st.session_state['essais']['Genre']==selection]['Nb ouvrages'].sum()/len(liste_ean)*100,0))}%]** des essais.")
 #
 # 				with st.container():
 
-# fig = plt.subplot()
 #
-# fig.add_trace(go.Pie(labels=country_group_df.pays_rework, values=country_group_df.nb_titre, name="Countries"))
-# fig.add_trace(go.Pie(labels=gender_viz_crew.gender_text, values=gender_viz_crew.nb_by_genre, name="Equipe tech"),
+# fig.add_trace(go.Pie(labels=gender_viz_crew.gender_text, values=gender_viz_crew.nb_by_genre, name="Equipe tech"),  # noqa: E501
 #              1, 2)
 #
-# colors0 = ['red' if x == 1 else 'blue' if x == 2 else 'green' if x == 0 else 'grey' for x in gender_viz_cast['gender']]
-# colors1 = ['red' if x == 1 else 'blue' if x == 2 else 'green' if x == 0 else 'grey' for x in gender_viz_crew['gender']]
 #
-# fig.update_traces(hole=.4, hoverinfo="label+value+name",marker=dict(colors=colors0)) # Use `hole` to create a donut-like pie chart&
 #
-# fig.update_traces(hole=.4, hoverinfo="label+value+name")#,marker=dict(colors=colors1), col=2)
 #
 # fig.update_layout(width = 400,
 # 			margin=dict(t=0, b=0, l=0, r=0),)
-# legend=dict(
-# 	orientation="h",
-# 	yanchor="bottom",
-# 	y=0.05,
-# 	xanchor="right",
 # 	x=1),
-# title=dict(text="Répartition par genre",y=0.9,),
 # Add annotations in the center of the donut pies.
 # annotations=[dict(text='Casting', x=0.15, y=0.5, font_size=20, showarrow=False),
 #             dict(text='Crew', x=0.83, y=0.5, font_size=20, showarrow=False)])
 #
-# 		st.plotly_chart(fig)
 #
-# 		fig1 = go.Figure(
 #    data=[go.Bar(x=country_group_df.pays_rework ,
 # 				 y=country_group_df.nb_titre)])
 #
-# 		#colinfofilm.write("Répartition par genre de l'équipe technique")
 # 		with colinfofilm.expander("Table de données"):
-# 			st.write("Casting")
-# 			st.dataframe(gender_viz_cast)
-# 			st.write("Equipe technique")
-# 			st.dataframe(gender_viz_crew)
-# 	placeholderviz.empty()
diff --git a/poetry.lock b/poetry.lock
index ac7d963..7d3fdc7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,49 @@
 # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
+[[package]]
+name = "altair"
+version = "5.3.0"
+description = "Vega-Altair: A declarative statistical visualization library for Python."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "altair-5.3.0-py3-none-any.whl", hash = "sha256:7084a1dab4d83c5e7e5246b92dc1b4451a6c68fd057f3716ee9d315c8980e59a"},
+    {file = "altair-5.3.0.tar.gz", hash = "sha256:5a268b1a0983b23d8f9129f819f956174aa7aea2719ed55a52eba9979b9f6675"},
+]
+
+[package.dependencies]
+jinja2 = "*"
+jsonschema = ">=3.0"
+numpy = "*"
+packaging = "*"
+pandas = ">=0.25"
+toolz = "*"
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.3.0)"]
+dev = ["geopandas", "hatch", "ipython", "m2r", "mypy", "pandas-stubs", "pytest", "pytest-cov", "ruff (>=0.3.0)", "types-jsonschema", "types-setuptools"]
+doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
+
+[[package]]
+name = "attrs"
+version = "23.2.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+]
+
+[package.extras]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
 [[package]]
 name = "bleach"
 version = "6.1.0"
@@ -18,6 +62,17 @@ webencodings = "*"
 [package.extras]
 css = ["tinycss2 (>=1.1.0,<1.3)"]
 
+[[package]]
+name = "blinker"
+version = "1.7.0"
+description = "Fast, simple object-to-object and broadcast signaling"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "blinker-1.7.0-py3-none-any.whl", hash = "sha256:c3f865d4d54db7abc53758a01601cf343fe55b84c1de4e3fa910e420b438d5b9"},
+    {file = "blinker-1.7.0.tar.gz", hash = "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"},
+]
+
 [[package]]
 name = "cachetools"
 version = "5.3.3"
@@ -161,6 +216,20 @@ files = [
     {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
 ]
 
+[[package]]
+name = "click"
+version = "8.1.7"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
+    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -172,6 +241,84 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.2.1"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.9"
+files = [
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 version = "2.5.35"
@@ -260,6 +504,58 @@ files = [
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 version = "1.6.12"
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 tqdm = "*"
 urllib3 = "*"
 
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 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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+python-versions = "!=3.9.7,>=3.8"
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+typing-extensions = ">=4.3.0,<5"
+watchdog = {version = ">=2.1.5", markers = "platform_system != \"Darwin\""}
+
+[package.extras]
+snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python (>=0.9.0)"]
+
+[[package]]
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+
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 version = "1.3"
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+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
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+
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 version = "2.0.1"
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+description = "List processing tools and functional utilities"
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+optional = false
+python-versions = ">= 3.8"
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+
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 version = "4.14.2"
@@ -720,6 +1767,17 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "typing-extensions"
+version = "4.11.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
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+
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 version = "2024.1"
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+optional = false
+python-versions = ">=3.8"
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+    {file = "watchdog-4.0.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:6e949a8a94186bced05b6508faa61b7adacc911115664ccb1923b9ad1f1ccf7b"},
+    {file = "watchdog-4.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:6a4db54edea37d1058b08947c789a2354ee02972ed5d1e0dca9b0b820f4c7f92"},
+    {file = "watchdog-4.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:d31481ccf4694a8416b681544c23bd271f5a123162ab603c7d7d2dd7dd901a07"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8fec441f5adcf81dd240a5fe78e3d83767999771630b5ddfc5867827a34fa3d3"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:6a9c71a0b02985b4b0b6d14b875a6c86ddea2fdbebd0c9a720a806a8bbffc69f"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:557ba04c816d23ce98a06e70af6abaa0485f6d94994ec78a42b05d1c03dcbd50"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:d0f9bd1fd919134d459d8abf954f63886745f4660ef66480b9d753a7c9d40927"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:f9b2fdca47dc855516b2d66eef3c39f2672cbf7e7a42e7e67ad2cbfcd6ba107d"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:73c7a935e62033bd5e8f0da33a4dcb763da2361921a69a5a95aaf6c93aa03a87"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:6a80d5cae8c265842c7419c560b9961561556c4361b297b4c431903f8c33b269"},
+    {file = "watchdog-4.0.0-py3-none-win32.whl", hash = "sha256:8f9a542c979df62098ae9c58b19e03ad3df1c9d8c6895d96c0d51da17b243b1c"},
+    {file = "watchdog-4.0.0-py3-none-win_amd64.whl", hash = "sha256:f970663fa4f7e80401a7b0cbeec00fa801bf0287d93d48368fc3e6fa32716245"},
+    {file = "watchdog-4.0.0-py3-none-win_ia64.whl", hash = "sha256:9a03e16e55465177d416699331b0f3564138f1807ecc5f2de9d55d8f188d08c7"},
+    {file = "watchdog-4.0.0.tar.gz", hash = "sha256:e3e7065cbdabe6183ab82199d7a4f6b3ba0a438c5a512a68559846ccb76a78ec"},
+]
+
+[package.extras]
+watchmedo = ["PyYAML (>=3.10)"]
+
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -782,4 +1881,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "416bb65f45809ba87534057885ffe5d6495d95dbbe6318d435829eedc509659e"
+content-hash = "5594949892f0ae826d11b880fe1811703ad7bab12982eb2877f5c6588bab265a"
diff --git a/pyproject.toml b/pyproject.toml
index af1ed29..8cacd33 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,10 @@ python = "^3.10"
 # ipykernel = "^5.3.4"
 pandas = "^2.2.0"
 kaggle = "^1.6.6"
+plotly = "^5.20.0"
+streamlit = "^1.33.0"
+matplotlib = "^3.8.4"
+ruff = "^0.3.7"
 
 [tool.poetry.group.dev.dependencies]
 pre-commit = "^2.20.0"