Ze1598

import pandas as pd
import numpy as np
import plotly.express as px
from typing import List, Union


def bin_ages(age_series: pd.Series) -> pd.Series:
    age_labels = [f"[{i}, {i+10})" for i in range(0, 91, 10)]

    age_bins = pd.IntervalIndex.from_tuples(

Ze1598

import pandas as pd
import plotly.graph_objects as go

# Load the resulting dataset
data = pd.read_csv("final_data.csv")

# Group by social networks AND gender
gender_socials = data.groupby(by=["Used Social Networks", "Gender"]).count()

# Unstack the genders, leaving only the social networks on the index\

Ze1598

import pandas as pd
from matplotlib import pyplot as plt

# Load the resulting dataset
data = pd.read_csv("final_data.csv")

# Group by social networks AND gender
gender_socials = data.groupby(by=["Used Social Networks", "Gender"]).count()

# Unstack the grouped DataFrame and plot it in a bar chart (the genders\

Ze1598

import pandas as pd
from typing import List
# Base clode for splitting and unpivoting a Series
# https://stackoverflow.com/questions/19482970/get-list-from-pandas-dataframe-column-headers


def unpivot_delimited_series(
    series: pd.Series,
    delimiter: str
) -> pd.DataFrame:

Ze1598

import pandas as pd
from typing import List
# Base clode for splitting and unpivoting a Series
# https://stackoverflow.com/questions/19482970/get-list-from-pandas-dataframe-column-headers


def unpivot_delimited_series(
    target_series: pd.Series,
    delimiter: str
) -> pd.DataFrame:

Ze1598

from functools import reduce

# Refactor of this function -> https://gist.github.com/Ze1598/cd32d9867dac11a029937539bac91721#file-get_spotify_playlist_data-py-L58-L73
def get_artist_counts(artists_info: List[List[Dict]]) -> Dict[str, int]:
    """
    Find the frequency of each artist featured in the playlist and return
    a dictionary of the type Artist:Frequency.
    """
    def map_fn(track_artists: List[Dict]) -> List[str]:
        """

Ze1598

[
    {
        "external_urls": {
            "spotify": "https://open.spotify.com/artist/6kNKUYGn6VNGsRoXmyoDPK"
        },
        "href": "https://api.spotify.com/v1/artists/6kNKUYGn6VNGsRoXmyoDPK",
        "id": "6kNKUYGn6VNGsRoXmyoDPK",
        "name": "The Amity Affliction",
        "type": "artist",
        "uri": "spotify:artist:6kNKUYGn6VNGsRoXmyoDPK"

Ze1598

{
    "album": {
        "album_type": "album",
        "(...)"
    },
    "artists": [
        {
            "external_urls": {
                "spotify": "https://open.spotify.com/artist/6kNKUYGn6VNGsRoXmyoDPK"
            },

Ze1598

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

# Load the data and sort it by artist frequency in descending order
def pre_process_data(file_name: str) -> pd.DataFrame:
    dataset = pd.read_csv(file_name)

    # Sort the dataset by "Frequency", in descending order, and reset the indices
    dataset = dataset.sort_values(

Ze1598

from os import environ
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
import pandas as pd
from typing import List, Dict
# https://github.com/plamere/spotipy/blob/master/examples/playlist_tracks.py


# Authenticate to Spotify
def authenticate(cliend_id: str, client_secret: str) -> spotipy.client.Spotify: