From 9a945da07f7bb969cf1c56f551d85189542b7282 Mon Sep 17 00:00:00 2001 From: Luis Arias Date: Tue, 16 Apr 2024 13:01:58 +0200 Subject: [PATCH] Add missing dependencies, fix linting errors, bump ruff to latest --- .pre-commit-config.yaml | 2 +- app/analyse_app_OLD.py | 19 +- app/odi_streamlit.py | 138 +++-- poetry.lock | 1101 ++++++++++++++++++++++++++++++++++++++- pyproject.toml | 4 + 5 files changed, 1178 insertions(+), 86 deletions(-) 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 = 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