Extracting and analyzing data form idescat.com

Requirements

import requests
import pandas as pd
pd.options.mode.chained_assignment = None 
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from bs4 import BeautifulSoup
sns.set_style("whitegrid")

Maria’s analysis in Catalonia

Extracting and processing the data

years = ['2018', '2019', '2020', '2021', '2022']
Maria_abs_freq, Maria_total_position, Maria_girls_position, Maria_total_percent, Maria_girls_percent = [], [], [], [], []

for year in years :

    url = f'https://api.idescat.cat/onomastica/v1/nadons/dades.json?id=40683&t={year}&lang=es'
    response = requests.get(url)
    data_json = response.json() 

    Maria_abs_freq.append(data_json['onomastica_nadons']['ff']['f']['pos1']['v']) # abs freq name Maria over the names of childs born in Catalonia in {year}
    Maria_total_position.append(data_json['onomastica_nadons']['ff']['f']['rank']['total']) # rank of name Maria over the names of childs born in Catalonia in {year}
    Maria_girls_position.append(data_json['onomastica_nadons']['ff']['f']['rank']['sex']) # rank of name Maria over the names of girls born in Catalonia in {year}
    Maria_total_percent.append(data_json['onomastica_nadons']['ff']['f']['pos1']['w']['total']) # % of childs born in Catalonia in {year} whose name is Maria
    Maria_girls_percent.append(data_json['onomastica_nadons']['ff']['f']['pos1']['w']['sex'])  # % of girls born in Catalonia in {year} whose name is Maria

Maria_dict = dict()
Maria_dict['years'] = years
Maria_dict['Maria_abs_freq'] = Maria_abs_freq
Maria_dict['Maria_total_position'] = Maria_total_position
Maria_dict['Maria_girls_position'] = Maria_girls_position
Maria_dict['Maria_total_percent'] = Maria_total_percent
Maria_dict['Maria_girls_percent'] = Maria_girls_percent

Maria_df = pd.DataFrame(Maria_dict)

for col_name in Maria_df.columns :
    if col_name in ['Maria_abs_freq', 'Maria_total_position', 'Maria_girls_position'] :
        Maria_df[col_name] = Maria_df[col_name].astype('int64')
    elif col_name in ['Maria_total_percent', 'Maria_girls_percent'] :
        Maria_df[col_name] = Maria_df[col_name].astype('float64')

# Data-frame with the data

print('Maria\'s analysis in Catalonia\n')
display(Maria_df)

Maria's analysis in Catalonia

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	466	8	4	7.38	15.15
1	2019	387	13	6	6.36	13.00
2	2020	318	20	10	5.49	11.23
3	2021	327	19	8	5.67	11.65
4	2022	267	28	13	4.74	9.83

Plotting the results

selected_columns = [x for x in Maria_df.columns if x != 'years']
titles = ["Absolute Frequencies Maria in Catalonia", "Ranking: Maria total position in Catalonia",
          "Ranking: Maria girls position in Catalonia", "Maria total % in Catalonia", "Maria girls % in Catalonia"]

# Define the number of rows and columns for the matrix plot
num_cols = 2  # You can adjust the number of columns as needed
num_rows = int(np.ceil(len(selected_columns) / num_cols))

# Create a subplot with the specified number of rows and columns
fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, 10))

# Flatten the axes array to make it easier to iterate
axes = axes.flatten()

colors = sns.color_palette("tab10", len(selected_columns))

# Loop through each 'geo' and create a subplot in the matrix
for (i, col_name), color, title in zip(enumerate(selected_columns), colors, titles) :
    ax = axes[i]  # Get the current axis
    sns.barplot(x="years", y=col_name, data=Maria_df, color=color, ax=ax)
    ax.set_title(f"{title}", fontsize=13)
    xticks_index = np.arange(0, len(Maria_df), 1)
    ax.set_xticks(xticks_index)
    ax.tick_params(axis='x', rotation=0)
    ax.set_xlabel('Year')
    ax.set_ylabel('')

# Remove any unused subplots in case the number of 'geo' values is less than num_rows * num_cols
for j in range(len(selected_columns), num_rows * num_cols):
    fig.delaxes(axes[j])

plt.suptitle('Maria\'s born in Catalonia - Statistical information\n Last five years (2018-2022)', fontsize=15, y=0.98, weight='bold') # Establishing a general tittle for the plot.
plt.subplots_adjust(hspace=0.5, wspace=0.25) # Adjust vertical (hspace) and horizontal (wspace) spacing
fig.savefig('Marias_Catalonia' + '.jpg', format='jpg', dpi=550)
# plt.tight_layout()
plt.show()

Maria’s analysis in Catalonia by city (comarca)

Extracting and processing the data

print('Maria\'s analysis in Catalonia by city (comarca)\n')

years = ['2018', '2019', '2020', '2021', '2022']
Maria_df_dict = dict()

for com_id in range(0, 42): # 42 = len(data_json['onomastica_nadons']['ff']['f']) - 1

    Maria_abs_freq, Maria_total_position, Maria_girls_position, Maria_total_percent, Maria_girls_percent = [], [], [], [], []

    for year in years :

        url = f'https://api.idescat.cat/onomastica/v1/nadons/dades.json?id=40683&class=com&t={year}&lang=es'
        response = requests.get(url)
        data_json = response.json() 

        Maria_abs_freq.append(data_json['onomastica_nadons']['ff']['f'][com_id]['pos1']['v']) # abs freq name Maria over the names of childs born in comarca {com_id} in {year}
        Maria_total_position.append(data_json['onomastica_nadons']['ff']['f'][com_id]['rank']['total']) # rank of name Maria over the names of childs born in comarca {com_id} in {year}
        Maria_girls_position.append(data_json['onomastica_nadons']['ff']['f'][com_id]['rank']['sex']) # rank of name Maria over the names of girls born in comarca {com_id} in {year}
        Maria_total_percent.append(data_json['onomastica_nadons']['ff']['f'][com_id]['pos1']['w']['total']) # % of childs born in comarca {com_id} in {year} whose name is Maria
        Maria_girls_percent.append(data_json['onomastica_nadons']['ff']['f'][com_id]['pos1']['w']['sex'])  # % of girls born in comarca {com_id} in {year} whose name is Maria

    Maria_dict = dict()
    Maria_dict['years'] = years
    Maria_dict['Maria_abs_freq'] = Maria_abs_freq
    Maria_dict['Maria_total_position'] = Maria_total_position
    Maria_dict['Maria_girls_position'] = Maria_girls_position
    Maria_dict['Maria_total_percent'] = Maria_total_percent
    Maria_dict['Maria_girls_percent'] = Maria_girls_percent  
    com = data_json['onomastica_nadons']['ff']['f'][com_id]['c']['content']
    Maria_df_dict[com] = pd.DataFrame(Maria_dict)

Maria's analysis in Catalonia by city (comarca)

###  Catalonia city (comarca) with more Maria\'s born by year

Maria_abs_freq_year = dict()

for year in ['2018', '2019', '2020', '2021', '2022']  :

    Maria_abs_freq_city = dict()

    for com in Maria_df_dict.keys() :

        value =  Maria_df_dict[com].loc[Maria_df_dict[com]['years'] == year, 'Maria_abs_freq'].iloc[0]

        if value != '_' : # To avoid the cities with missing value.
        
            Maria_abs_freq_city[com] = value

    Maria_abs_freq_year[year] = Maria_abs_freq_city


Maria_abs_freq_values = dict()
Maria_abs_freq_index = dict()
city_max_Maria = dict()
years =  ['2018', '2019', '2020', '2021', '2022'] 

for year in years :

    Maria_abs_freq_values[year] = np.array([x for x in Maria_abs_freq_year[year].values()], dtype=int)
    Maria_abs_freq_index[year] = np.array([x for x in Maria_abs_freq_year[year].keys()])
    city_max_Maria[year] = Maria_abs_freq_index[year][np.argmax(Maria_abs_freq_values[year])]
    print(f'Catalonia city (comarca) with more Maria\'s born in {year} -->', city_max_Maria[year])

Catalonia city (comarca) with more Maria's born in 2018 --> Barcelonès
Catalonia city (comarca) with more Maria's born in 2019 --> Barcelonès
Catalonia city (comarca) with more Maria's born in 2020 --> Barcelonès
Catalonia city (comarca) with more Maria's born in 2021 --> Barcelonès
Catalonia city (comarca) with more Maria's born in 2022 --> Barcelonès

Plotting the results

# Define the number of rows and columns for the matrix plot
num_cols = 3  # You can adjust the number of columns as needed
num_rows = int(np.ceil(len(years) / num_cols))

# Create a subplot with the specified number of rows and columns
fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, 13))

# Flatten the axes array to make it easier to iterate
axes = axes.flatten()

colors = sns.color_palette("tab10", len(years))

# Loop through each 'geo' and create a subplot in the matrix
for i, color, year in zip(range(0,len(years)), colors, years) :

    ax = axes[i]  # Get the current axis
    sns.barplot(x=Maria_abs_freq_values[year], y=Maria_abs_freq_index[year], color=color, ax=ax)
    ax.set_title(f"{year}", fontsize=13)
    ax.tick_params(axis='x', rotation=0)
    ax.set_xlabel('Count')
    ax.set_ylabel('Cities')

# Remove any unused subplots in case the number of 'geo' values is less than num_rows * num_cols
for j in range(len(years), num_rows * num_cols):
    fig.delaxes(axes[j])

plt.suptitle('Absolute Frequencies Maria by Catalonia cities (comarcas)\n Last five years (2018-2022)', fontsize=15, y=0.95, weight='bold') # Establishing a general tittle for the plot.
plt.subplots_adjust(hspace=0.25, wspace=0.8) # Adjust vertical (hspace) and horizontal (wspace) spacing
# fig.savefig('Marias_Areas_Catalonia' + '.jpg', format='jpg', dpi=550)
# plt.tight_layout()
plt.show()

Gender info for the childs born in Catalonia in the last nine years (2014-2022)

Extracting and processing the data

def try_float(x):   
    try:
        return float(x)
    except ValueError:
        return None

url = 'https://www.idescat.cat/indicadors/?id=aec&n=15237&fil=43&lang=en'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

table = soup.find('table')

columns = table.find(class_="cap") 
columns_html = columns.select("thead th")
columns_list = []

for i in range(1,len(columns_html)) :
    columns_list.append(columns.select("thead th")[i].text)

columns_list = ['Year'] + columns_list
tr_list = table.select('tr')
useful_info_index = range(1,(len(tr_list)-3))

rows = dict()
for i , r in enumerate(useful_info_index) :
    text_data = tr_list[r].text
    row_data = text_data.replace('\n', ' ').split()
    row_data = [x.replace(',', '.') for x in row_data]
    row_data = [try_float(x) for x in row_data]
    row_data = [x for x in row_data if x != None]
    rows[i] = row_data

df = pd.DataFrame(rows)
df = df.T
df.columns = columns_list
df['Year'] = df['Year'].astype('int')
df['Boys_prop'] = round(df['Boys'] / df['Total'], 3)
df['Girls_prop'] = round(df['Girls'] / df['Total'], 3)

# Data-frame with the data

print('Gender info for the childs born in Catalonia in the last nine years (2014-2022)\n')
display(df)

Gender info for the childs born in Catalonia in the last nine years (2014-2022)

	Year	Boys	Girls	Total	Boys_prop	Girls_prop
0	2022	29.187	27.195	56.382	0.518	0.482
1	2021	29.604	28.030	57.634	0.514	0.486
2	2020	29.832	28.632	58.464	0.510	0.490
3	2019	31.453	30.095	61.548	0.511	0.489
4	2018	32.623	30.943	63.566	0.513	0.487
5	2017	34.462	32.341	66.803	0.516	0.484
6	2016	35.435	33.539	68.974	0.514	0.486
7	2015	36.406	34.044	70.450	0.517	0.483

Plotting the results

selected_columns = [x for x in df.columns if x in ['Boys_prop', 'Girls_prop']]

# Define the number of rows and columns for the matrix plot
num_cols = 2  # You can adjust the number of columns as needed
num_rows = int(np.ceil(len(selected_columns) / num_cols))

# Create a subplot with the specified number of rows and columns
fig, axes = plt.subplots(num_rows, num_cols, figsize=(8, 3))

# Flatten the axes array to make it easier to iterate
axes = axes.flatten()

colors = sns.color_palette("tab10", len(selected_columns))
titles = ['Girls', 'Boys']

# Loop through each 'geo' and create a subplot in the matrix
for i, color, col_name, title in zip(range(0,len(selected_columns)), colors, selected_columns, titles) :
    ax = axes[i]  # Get the current axis
    sns.barplot(x="Year", y=col_name, data=df, color=color, ax=ax)
    ax.set_title(f"{title}", fontsize=11)
    xticks_index = np.arange(0, len(df), 1)
    ax.set_xticks(xticks_index)
    ax.tick_params(axis='x', rotation=0, labelsize=9)
    ax.tick_params(axis='y', rotation=0, labelsize=9)
    ax.set_xlabel('Year')
    ax.set_ylabel(' ')
    ax.set_ylim([0.45, 0.54])

# Remove any unused subplots in case the number of 'geo' values is less than num_rows * num_cols
for j in range(len(selected_columns), num_rows * num_cols):
    fig.delaxes(axes[j])

plt.suptitle('Relative frequencies of the gender of childs born in Catalonia\n Last nine years (2014-2022)', fontsize=12, y=1.1, weight='bold') # Establishing a general tittle for the plot.
plt.subplots_adjust(hspace=0.6, wspace=0.3) # Adjust vertical (hspace) and horizontal (wspace) spacing
# fig.savefig('Gender_born_Catalonia' + '.jpg', format='jpg', dpi=550)
# plt.tight_layout()
plt.show()

Maria’s analysis in Catalonia by province

Extracting and processing the data

years = ['2018', '2019', '2020', '2021', '2022']
Maria_df_dict = dict()

for prov_id in range(0, 4): # 4 = len(data_json['onomastica_nadons']['ff']['f']) - 1

    Maria_abs_freq, Maria_total_position, Maria_girls_position, Maria_total_percent, Maria_girls_percent = [], [], [], [], []

    for year in years :

        url = f'https://api.idescat.cat/onomastica/v1/nadons/dades.json?id=40683&class=prov&t={year}&lang=es'
        response = requests.get(url)
        data_json = response.json() 

        Maria_abs_freq.append(data_json['onomastica_nadons']['ff']['f'][prov_id]['pos1']['v']) # abs freq name Maria over the names of childs born in province {prov_id} in {year}
        Maria_total_position.append(data_json['onomastica_nadons']['ff']['f'][prov_id]['rank']['total']) # rank of name Maria over the names of childs born in province {prov_id} in {year}
        Maria_girls_position.append(data_json['onomastica_nadons']['ff']['f'][prov_id]['rank']['sex']) # rank of name Maria over the names of girls born in province {prov_id} in {year}
        Maria_total_percent.append(data_json['onomastica_nadons']['ff']['f'][prov_id]['pos1']['w']['total']) # % of childs born in province {prov_id} in {year} whose name is Maria
        Maria_girls_percent.append(data_json['onomastica_nadons']['ff']['f'][prov_id]['pos1']['w']['sex'])  # % of girls born in province {prov_id} in {year} whose name is Maria

    Maria_dict = dict()
    Maria_dict['years'] = years
    Maria_dict['Maria_abs_freq'] = Maria_abs_freq
    Maria_dict['Maria_total_position'] = Maria_total_position
    Maria_dict['Maria_girls_position'] = Maria_girls_position
    Maria_dict['Maria_total_percent'] = Maria_total_percent
    Maria_dict['Maria_girls_percent'] = Maria_girls_percent  
    prov = data_json['onomastica_nadons']['ff']['f'][prov_id]['c']['content']
    Maria_df_dict[prov] = pd.DataFrame(Maria_dict)

print('Maria\'s analysis in Catalonia by province')

for prov in Maria_df_dict.keys():
    print('-----------------------------------------------------------------------------------------------')
    print(prov)
    display(Maria_df_dict[prov])

Maria's analysis in Catalonia by province
-----------------------------------------------------------------------------------------------
Barcelona

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	344	11	5	7.41	15.19
1	2019	288	14	7	6.46	13.17
2	2020	245	19	9	5.81	11.89
3	2021	_	_	_	_	_
4	2022	189	33	14	4.59	9.56

-----------------------------------------------------------------------------------------------
Girona

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	47	6	2	7.00	14.43
1	2019	27	31	16	4.21	8.62
2	2020	33	21	11	5.24	10.79
3	2021	37	12	5	6.10	12.45
4	2022	19	47	22	3.21	6.52

-----------------------------------------------------------------------------------------------
Lleida

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	17	28	13	4.89	10.15
1	2019	23	8	3	6.69	13.71
2	2020	8	69	38	2.44	4.84
3	2021	17	19	7	5.30	11.06
4	2022	19	15	7	5.89	12.22

-----------------------------------------------------------------------------------------------
Tarragona

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	58	4	2	8.81	18.22
1	2019	49	6	4	7.60	15.85
2	2020	32	19	8	5.23	10.62
3	2021	30	26	12	5.00	10.33
4	2022	40	10	5	6.66	13.70

# Catalonia province with more Maria's born in 2022

Maria_abs_freq_2022 = dict()

for prov in Maria_df_dict.keys():
    Maria_abs_freq_2022[prov] = Maria_df_dict[prov].loc[Maria_df_dict[prov]['years'] == '2022', 'Maria_abs_freq'].iloc[0]

Maria_abs_freq_2022_values = np.array([x for x in Maria_abs_freq_2022.values()], dtype=int)
Maria_abs_freq_2022_index = np.array([x for x in Maria_abs_freq_2022.keys()])
prov_max_Maria_2022 = Maria_abs_freq_2022_index[np.argmax(Maria_abs_freq_2022_values)]
print('Catalonia province with more Maria\'s born in 2022 -->', prov_max_Maria_2022)

Catalonia province with more Maria's born in 2022 --> Barcelona

Plotting the results

Maria_df_dict['Barcelona'] = Maria_df_dict['Barcelona'].drop([3], axis=0)

for prov in Maria_df_dict.keys() :
    for col_name in Maria_df_dict[prov].columns :
        if col_name in ['Maria_abs_freq', 'Maria_total_position', 'Maria_girls_position'] :
            Maria_df_dict[prov][col_name] = Maria_df_dict[prov][col_name].astype('int64')
        elif col_name in ['Maria_total_percent', 'Maria_girls_percent'] :
            Maria_df_dict[prov][col_name] = Maria_df_dict[prov][col_name].astype('float64')

provinces = Maria_df_dict.keys()

# Define the number of rows and columns for the matrix plot
num_cols = 2  # You can adjust the number of columns as needed
num_rows = int(np.ceil(len(provinces) / num_cols))

# Create a subplot with the specified number of rows and columns
fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 8))

# Flatten the axes array to make it easier to iterate
axes = axes.flatten()

colors = sns.color_palette("tab10", len(provinces))

# Loop through each 'geo' and create a subplot in the matrix
for i, color, prov in zip(range(0,len(provinces)), colors, provinces) :
    ax = axes[i]  # Get the current axis
    sns.barplot(x="years", y='Maria_abs_freq', data=Maria_df_dict[prov], color=color, ax=ax)
    ax.set_title(f"{prov}", fontsize=12)
    xticks_index = np.arange(0, len(Maria_df_dict[prov]), 1)
    ax.set_xticks(xticks_index)
    ax.tick_params(axis='x', rotation=0)
    ax.set_xlabel('Year')
    ax.set_ylabel('Count')

# Remove any unused subplots in case the number of 'geo' values is less than num_rows * num_cols
for j in range(len(provinces), num_rows * num_cols):
    fig.delaxes(axes[j])

plt.suptitle('Absolute Frequencies Maria by Catalonia provinces\n Last five years (2018-2022)', fontsize=15, y=0.98, weight='bold') # Establishing a general tittle for the plot.
plt.subplots_adjust(hspace=0.3, wspace=0.3) # Adjust vertical (hspace) and horizontal (wspace) spacing
# fig.savefig('Marias_Provinces_Catalonia' + '.jpg', format='jpg', dpi=550)
# plt.tight_layout()
plt.show()

Maria’s analysis in Catalonia by area (region)

Extracting and processing the data

years = ['2018', '2019', '2020', '2021', '2022']
Maria_df_dict = dict()

for at_id in range(0, 8): # 8 = len(data_json['onomastica_nadons']['ff']['f']) - 1

    Maria_abs_freq, Maria_total_position, Maria_girls_position, Maria_total_percent, Maria_girls_percent = [], [], [], [], []

    for year in years :

        url = f'https://api.idescat.cat/onomastica/v1/nadons/dades.json?id=40683&class=at&t={year}&lang=es'
        response = requests.get(url)
        data_json = response.json() 

        Maria_abs_freq.append(data_json['onomastica_nadons']['ff']['f'][at_id]['pos1']['v']) # abs freq name Maria over the names of childs born in comarca {com_id} in {year}
        Maria_total_position.append(data_json['onomastica_nadons']['ff']['f'][at_id]['rank']['total']) # rank of name Maria over the names of childs born in comarca {com_id} in {year}
        Maria_girls_position.append(data_json['onomastica_nadons']['ff']['f'][at_id]['rank']['sex']) # rank of name Maria over the names of girls born in comarca {com_id} in {year}
        Maria_total_percent.append(data_json['onomastica_nadons']['ff']['f'][at_id]['pos1']['w']['total']) # % of childs born in comarca {com_id} in {year} whose name is Maria
        Maria_girls_percent.append(data_json['onomastica_nadons']['ff']['f'][at_id]['pos1']['w']['sex'])  # % of girls born in comarca {com_id} in {year} whose name is Maria

    Maria_dict = dict()
    Maria_dict['years'] = years
    Maria_dict['Maria_abs_freq'] = Maria_abs_freq
    Maria_dict['Maria_total_position'] = Maria_total_position
    Maria_dict['Maria_girls_position'] = Maria_girls_position
    Maria_dict['Maria_total_percent'] = Maria_total_percent
    Maria_dict['Maria_girls_percent'] = Maria_girls_percent  
    at = data_json['onomastica_nadons']['ff']['f'][at_id]['c']['content']
    Maria_df_dict[at] = pd.DataFrame(Maria_dict)

print('Maria\'s analysis in Catalonia by area (region)')

for at in Maria_df_dict.keys():
    print('-----------------------------------------------------------------------------------------------')
    print(at)
    display(Maria_df_dict[at])

Maria's analysis in Catalonia by area (region)
-----------------------------------------------------------------------------------------------
Metropolità

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	308	10	5	7.67	15.66
1	2019	246	15	7	6.38	13.01
2	2020	210	22	10	5.77	11.80
3	2021	211	20	8	5.77	11.86
4	2022	164	30	13	4.63	9.63

-----------------------------------------------------------------------------------------------
Comarques Gironines

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	47	6	2	7.11	14.67
1	2019	27	31	16	4.29	8.77
2	2020	33	21	11	5.30	10.90
3	2021	37	12	5	6.21	12.69
4	2022	19	44	20	3.27	6.65

-----------------------------------------------------------------------------------------------
Camp de Tarragona

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	39	5	3	8.72	18.03
1	2019	35	3	2	8.26	17.34
2	2020	18	28	13	4.42	8.89
3	2021	17	37	18	4.27	8.89
4	2022	30	5	3	7.60	15.50

-----------------------------------------------------------------------------------------------
Terres de l'Ebre

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	14	7	4	10.52	21.41
1	2019	11	9	6	8.12	16.69
2	2020	5	30	16	4.13	8.17
3	2021	10	12	6	8.26	16.34
4	2022	5	37	15	4.00	8.35

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Ponent

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	14	31	13	4.65	9.60
1	2019	20	7	4	6.78	13.93
2	2020	7	68	34	2.46	4.87
3	2021	16	15	5	5.76	11.98
4	2022	16	15	8	5.71	11.85

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Comarques Centrals

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	22	18	8	6.46	13.56
1	2019	29	7	2	8.73	17.91
2	2020	17	19	7	5.40	11.14
3	2021	17	23	11	5.52	11.09
4	2022	17	22	9	5.46	11.36

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Alt Pirineu i Aran

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	_	_	_	_	_
1	2019	_	_	_	_	_
2	2020	_	_	_	_	_
3	2021	0	_	_	0.00	0.00
4	2022	_	_	_	_	_

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Penedès

	years	Maria_abs_freq	Maria_total_position	Maria_girls_position	Maria_total_percent	Maria_girls_percent
0	2018	20	23	12	5.38	11.22
1	2019	18	24	12	4.85	9.91
2	2020	27	4	3	7.59	15.86
3	2021	16	38	16	4.32	9.09
4	2022	15	38	18	4.26	8.87

# Catalonia region with more Maria\'s born in 2022

selected_at = [x for x in Maria_df_dict.keys() if x != 'Alt Pirineu i Aran']
Maria_abs_freq_2022 = dict()

for at in selected_at :
    Maria_abs_freq_2022[at] = Maria_df_dict[at].loc[Maria_df_dict[at]['years'] == '2022', 'Maria_abs_freq'].iloc[0]

Maria_abs_freq_2022_values = np.array([x for x in Maria_abs_freq_2022.values()], dtype=int)
Maria_abs_freq_2022_index = np.array([x for x in Maria_abs_freq_2022.keys()])
prov_max_Maria_2022 = Maria_abs_freq_2022_index[np.argmax(Maria_abs_freq_2022_values)]
print('Catalonia area with more Maria\'s born in 2022 -->', prov_max_Maria_2022)

Catalonia area with more Maria's born in 2022 --> Metropolità

Plotting the results

for at in selected_at :
    for col_name in Maria_df_dict[at].columns :
        if col_name in ['Maria_abs_freq', 'Maria_total_position', 'Maria_girls_position'] :
            Maria_df_dict[at][col_name] = Maria_df_dict[at][col_name].astype('int64')
        elif col_name in ['Maria_total_percent', 'Maria_girls_percent'] :
            Maria_df_dict[at][col_name] = Maria_df_dict[at][col_name].astype('float64')

# Define the number of rows and columns for the matrix plot
num_cols = 2  # You can adjust the number of columns as needed
num_rows = int(np.ceil(len(selected_at) / num_cols))

# Create a subplot with the specified number of rows and columns
fig, axes = plt.subplots(num_rows, num_cols, figsize=(11, 10))

# Flatten the axes array to make it easier to iterate
axes = axes.flatten()

colors = sns.color_palette("tab10", len(selected_at))

# Loop through each 'geo' and create a subplot in the matrix
for i, color, at in zip(range(0,len(selected_at)), colors, selected_at) :
    ax = axes[i]  # Get the current axis
    sns.barplot(x="years", y='Maria_abs_freq', data=Maria_df_dict[at], color=color, ax=ax)
    ax.set_title(f"{at}", fontsize=13)
    xticks_index = np.arange(0, len(Maria_df_dict[at]), 1)
    ax.set_xticks(xticks_index)
    ax.tick_params(axis='x', rotation=0)
    ax.set_xlabel('Year')
    ax.set_ylabel('Count')

# Remove any unused subplots in case the number of 'geo' values is less than num_rows * num_cols
for j in range(len(selected_at), num_rows * num_cols):
    fig.delaxes(axes[j])

plt.suptitle('Absolute Frequencies Maria by Catalonia areas - Last five years (2018-2022)', fontsize=15, y=0.95, weight='bold') # Establishing a general tittle for the plot.
plt.subplots_adjust(hspace=0.8, wspace=0.3) # Adjust vertical (hspace) and horizontal (wspace) spacing
# fig.savefig('Marias_Areas_Catalonia' + '.jpg', format='jpg', dpi=550)
# plt.tight_layout()
plt.show()