Pre Analysis

Preliminar analysis after cleaning data

This notebook was created by a previous bachelor student who used for his/her thesis

Preliminary data analysis - Blood Donations in FVG

Now that the data is cleaned and ready to be analyzed, it’s possible to begin with some basic tasks.

Dataframe importing

First of all, let’s import the needed libraries and the dataset:

import pandas as pd
import matplotlib.pyplot as plt

combined_dataframe = pd.read_csv('../../DATA/FINAL/dataframe_cleaned.csv')

# Verifying every NaN has been removed:
print(combined_dataframe.isnull().sum())

# Printing the first couple lines to check the dataset's format:
combined_dataframe.head()

donor_class              0
donation_type            0
birth_year               0
birth_cohort             0
first_donation_year      0
first_donation_cohort    0
number_of_donations      0
gender                   0
year                     0
age                      0
unique_number            0
dtype: int64

	donor_class	donation_type	birth_year	birth_cohort	first_donation_year	first_donation_cohort	number_of_donations	gender	year	age	unique_number
0	O	SANGUE	1980.0	1980.0	2009.0	2005.0	1.0	F	2009.0	29.0	26934057.0
1	P	PLASMAF	1980.0	1980.0	2005.0	2005.0	5.0	M	2009.0	29.0	26826075.0
2	P	PLASMAF	1980.0	1980.0	2005.0	2005.0	5.0	M	2009.0	29.0	26826075.0
3	P	PLASMAF	1980.0	1980.0	2005.0	2005.0	5.0	M	2009.0	29.0	26826075.0
4	P	PLASMAF	1980.0	1980.0	2005.0	2005.0	5.0	M	2009.0	29.0	26826075.0

Gender Distribution

import matplotlib.pyplot as plt

# Counting the number of male and female donors:
gender_counts = combined_dataframe["gender"].value_counts()

# Plotting the gender distribution:
plt.figure(figsize=(6, 4))
gender_counts.plot(kind="bar", color=["lightblue", "pink"])
plt.title("Gender Distribution of Donors")
plt.xlabel("Gender")
plt.ylabel("Number of Donations")
plt.xticks(rotation=0)
plt.show()

# Showing the raw numbers of male and female donors:
print(gender_counts)

gender
M    197761
F     70769
Name: count, dtype: int64

The plot shows how the gender of donators is for the vast majority of males, almost three times as many as females.

Age Distribution

# Plotting the age distribution for donors:
plt.figure(figsize=(10, 6))
combined_dataframe["age"].hist(bins=30, color="lightgreen", edgecolor="black")
plt.title("Age Distribution of Donors (Age 18-75)")
plt.xlabel("Age")
plt.ylabel("Number of Donations")

# Set the x-axis limit to start at 18 and end at 70 - it's impossible to donate outside this age gap in Italy: 
plt.xlim(18, 70)

plt.show()

# Printing the average age:
print(f"Average donor age: {combined_dataframe["age"].mean():.2f}")

Average donor age: 41.95

Number of Donations per Donor

# Grouping the data by unique_number and year to calculate the number of donations per donor per year:
donations_per_donor_per_year = combined_dataframe.groupby(['unique_number', 'year']).size().reset_index(name='num_donations')

# Grouping by 'num_donations' to count how many donors donated a specific number of times, then average across years:
average_donations = donations_per_donor_per_year.groupby('num_donations').size().reset_index(name='num_people')
average_donations['num_people'] = average_donations['num_people'] / len(donations_per_donor_per_year['year'].unique())

# Plotting the average number of donors who donated a certain number of times:
plt.figure(figsize=(10, 6))
plt.bar(average_donations['num_donations'], average_donations['num_people'], color='skyblue', edgecolor='black')
plt.title('Average Number of Donors by Number of Donations Per Year')
plt.xlabel('Number of Donations per Year')
plt.ylabel('Average Number of Donors')
plt.yscale('log') # this will change in later patches
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

TODO change tis plot to make it more readable

Type of donations

donation_type_counts = combined_dataframe["donation_type"].value_counts()

plt.figure(figsize=(8, 5))
donation_type_counts.plot(kind="bar", color="purple")
plt.title("Distribution of Donation Types")
plt.xlabel("Donation Type")
plt.ylabel("Number of Donations")
plt.xticks(rotation=45)
plt.show()