import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

/var/folders/gc/0752xrm56pnf0r0dsrn5370c0000gr/T/ipykernel_71803/555797462.py:1: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
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  import pandas as pd

#Loading the dataset
data = pd.read_csv("Death_rates_for_suicide__by_sex__race__Hispanic_origin__and_age__United_States.csv")

What the code below does:

The code below filters the dataset to include only the rows where the 'STUB_LABEL' column contains the string 'Male: Black or African American' and the 'YEAR' column falls within the range of 1985 to 2018. It then creates a new DataFrame called Black_Male_Data containing these filtered rows. Finally, it displays a subset of columns ('INDICATOR', 'UNIT', 'STUB_LABEL', 'AGE', 'YEAR', 'ESTIMATE') from the filtered dataset.

Black_Male_Data = data[(data['STUB_LABEL'].str.contains('Male: Black or African American')) & 
                       (data['YEAR'].between(1985, 2018))]
                       
Black_Male_Data[['INDICATOR', 'UNIT', 'STUB_LABEL', 'AGE', 'YEAR', 'ESTIMATE']]

	INDICATOR	UNIT	STUB_LABEL	AGE	YEAR	ESTIMATE
218	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 15-24 years	15-24 years	1985	13.3
219	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 15-24 years	15-24 years	1986	11.4
220	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 15-24 years	15-24 years	1987	12.9
221	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 15-24 years	15-24 years	1988	14.5
222	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 15-24 years	15-24 years	1989	16.6
...	...	...	...	...	...	...
416	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 75-84 years	75-84 years	2014	11.0
417	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 75-84 years	75-84 years	2015	11.2
418	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 75-84 years	75-84 years	2016	10.6
419	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 75-84 years	75-84 years	2017	8.8
420	Death rates for suicide	Deaths per 100,000 resident population, crude	Male: Black or African American: 75-84 years	75-84 years	2018	10.7

171 rows × 6 columns

The code below does the exact same thing as the code above, however, it filters the dataset to include only the rows where the 'STUB_LABEL' column contains the string 'Female: Black or African American'

# Filter the dataset to include males who are Black or African American and the years 1985 and 2018
Black_Female_Data = data[(data['STUB_LABEL'].str.contains('Female: Black or African American')) & 
                       (data['YEAR'].between(1985, 2018))]

# Display the filtered dataset including the INDICATOR, UNIT, AGE, YEAR, and ESTIMATE columns
Black_Female_Data[['INDICATOR', 'UNIT', 'STUB_LABEL', 'AGE', 'YEAR', 'ESTIMATE']]

	INDICATOR	UNIT	STUB_LABEL	AGE	YEAR	ESTIMATE
806	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 15-24 years	15-24 years	1985	2.0
807	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 15-24 years	15-24 years	1986	2.3
808	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 15-24 years	15-24 years	1987	2.5
809	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 15-24 years	15-24 years	1988	2.6
810	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 15-24 years	15-24 years	1989	2.9
...	...	...	...	...	...	...
920	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 45-64 years	45-64 years	2015	2.6
921	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 45-64 years	45-64 years	2016	2.3
922	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 45-64 years	45-64 years	2017	2.6
923	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 45-64 years	45-64 years	2018	3.0
924	Death rates for suicide	Deaths per 100,000 resident population, crude	Female: Black or African American: 45-64 years	45-64 years	2018	5.5

103 rows × 6 columns

What the code below does:

It visualizes the suicide estimates over time for Black males and females, by creating a single plot showing all the suicide estimates over time, categorized by age.

# Create a single plot
plt.figure(figsize=(12, 8))

# Define colors for each age category
colors = ['blue', 'green', 'red', 'orange', 'purple', 'cyan', 'magenta', 'yellow', 'lime', 'pink']

# Plot line plots for each age category for Black Males
for i, age_category in enumerate(['15-24 years', '25-44 years', '45-64 years', '65-74 years', '75-84 years']):
    # Filter data for Black Males of the current age category
    male_data = Black_Male_Data[Black_Male_Data['AGE'] == age_category]
    
    # Plot line plot for Black Males of the current age category with a different color
    plt.plot(male_data['YEAR'], male_data['ESTIMATE'], color=colors[i], marker='o', label=f'Black Males ({age_category})')

# Plot line plots for each age category for Black Females
for i, age_category in enumerate(['15-24 years', '25-44 years', '45-64 years', '65-74 years', '75-84 years']):
    # Filter data for Black Females of the current age category
    female_data = Black_Female_Data[Black_Female_Data['AGE'] == age_category]
    
    # Plot line plot for Black Females of the current age category with a different color
    plt.plot(female_data['YEAR'], female_data['ESTIMATE'], color=colors[i+len(colors)//2], marker='o', linestyle='--', label=f'Black Females ({age_category})')

# Set titles and labels
plt.title('Suicide Estimates Over Time for Black Males and Females by Age Category')
plt.xlabel('Year')
plt.ylabel('Suicide Estimate (% per 100,000 resident population)')

# Add legend
plt.legend()

# Show the plot
plt.grid(True)
plt.tight_layout()
plt.show()