Commit 82dbecf5 authored by Maximilian Dolling's avatar Maximilian Dolling
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obey the linter

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"""
This script analysis a data set about astronauts and creates different
plots as result.
"""
from datetime import date
from os import makedirs
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
plt.style.use("ggplot")
_ASTRONAUT_DATA = "data/astronauts.json"
_OUTPUT_PATH = "results"
##
# Data preparation functions
##
def prepare_data_set(data_frame: pd.DataFrame) -> pd.DataFrame:
"""
Prepares the raw data by:
- dropping NaN's
- setting data types
- calculating some extra columns
Args:
data_frame: A pandas DataFrame.
Returns:
A pandas DataFrame with preprocessed data.
"""
data_frame = rename_columns(data_frame)
data_frame = data_frame.set_index("astronaut_id")
# Set pandas dtypes for columns with date or time
data_frame = data_frame.dropna(subset=["time_in_space"])
data_frame["time_in_space"] = data_frame["time_in_space"].astype(int)
data_frame["time_in_space"] = pd.to_timedelta(data_frame["time_in_space"], unit="m")
data_frame["birthdate"] = pd.to_datetime(data_frame["birthdate"])
data_frame["date_of_death"] = pd.to_datetime(data_frame["date_of_death"])
data_frame.sort_values("birthdate", inplace=True)
# Calculate extra columns from the original data
data_frame["time_in_space_D"] = data_frame["time_in_space"].astype("timedelta64[D]")
data_frame["alive"] = data_frame["date_of_death"].apply(is_alive)
data_frame["age"] = data_frame["birthdate"].apply(calculate_age)
data_frame["died_with_age"] = data_frame.apply(died_with_age, axis=1)
return data_frame
def rename_columns(data_frame):
"""
The original column naming in the data set is not useful
for programming with pandas. So we rename it.
"""
name_mapping = {
"astronaut": "astronaut_id",
"astronautLabel": "name",
"birthplaceLabel": "birthplace",
"sex_or_genderLabel": "sex_or_gender",
}
data_frame = data_frame.rename(index=str, columns=name_mapping)
return data_frame
def is_alive(date_of_death) -> bool:
"""
Checks, if 'date_of_death' exists or not.
Args:
date_of_death: Either a pandas NaTType or a pandas Timestamp.
Returns:
bool
"""
if pd.isnull(date_of_death):
return True
return False
def calculate_age(born: pd.Timestamp) -> int:
"""
Calculates an age from a date.
Args:
born: pandas.Timestamp
Returns:
int
"""
today = date.today()
return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
def died_with_age(row: pd.Series):
"""
Calculates an age from a birthdate and date_of_death.
Args:
row: pandas.Series with the columns `birthdate` and `date_of_death`
Returns:
int
"""
if pd.isnull(row["date_of_death"]):
return None
born = row["birthdate"]
today = row["date_of_death"]
return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
##
# Plot functions
##
def create_time_of_x_in_space(data_frame, filename, title):
"""
This function generated a plot with the summed up time of 'living beings'
in space over the years by their birthday's.
"""
reduced_data_frame = data_frame[["birthdate", "time_in_space", "time_in_space_D"]].copy()
reduced_data_frame["accumulated_time_in_minutes"] = reduced_data_frame["time_in_space"].cumsum()
reduced_data_frame["accumulated_time_in_days"] = reduced_data_frame["time_in_space_D"].cumsum()
axs = reduced_data_frame.plot(x="birthdate", y="accumulated_time_in_days")
axs.set_title(title)
axs.set_xlabel("Years ")
axs.set_ylabel("t in days")
save(axs.get_figure(), filename)
def create_age_histogram(age_data_frame, died_data_frame):
"""
The function generates a combined histogram of astronauts
in the categories 'age at dead' and 'age alive'.
"""
fig, axs = plt.subplots(1, 1)
axs.hist(
[died_data_frame["died_with_age"], age_data_frame["age"]],
bins=70,
range=(31, 100),
stacked=True,
)
axs.set_xlabel("Age")
axs.set_ylabel("Number of astronauts")
axs.set_title("Dead vs. Alive astronauts")
save(fig, "combined_histogram.png")
def create_age_boxplot(age_data_frame, died_data_frame):
"""
The function generates a boxplot of astronauts age distribution
in the categories dead and alive.
"""
fig, axs = plt.subplots(1, 1)
axs.boxplot([died_data_frame["died_with_age"], age_data_frame["age"]])
axs.set_title("Age distribution; Dead vs. Alive astronauts")
axs.set_xlabel("Category")
plt.setp(axs, xticks=[1, 2], xticklabels=["Dead", "Alive"])
axs.set_ylabel("Age")
save(fig, "boxplot.png")
def save(fig: plt.Figure, filename: str):
"""
Saves a matplotlib Figure to a file. It overwrites existing files with the same filename.
Args:
fig: matplotlib.pyplot.Figure
filename: str
"""
fig.savefig(Path(_OUTPUT_PATH).resolve() / Path(filename))
def perform_analysis():
""" Glues data preparation and plotting. """
# Set up directory structure and preprocess data
makedirs(_OUTPUT_PATH, exist_ok=True)
data_frame = pd.read_json(Path(_ASTRONAUT_DATA).resolve())
data_frame = prepare_data_set(data_frame)
# Male humans in space
data_frame_male = data_frame.loc[
data_frame["sex_or_gender"] == "male", ["birthdate", "time_in_space", "time_in_space_D"]
].copy()
create_time_of_x_in_space(
data_frame_male,
"male_humans_in_space.png",
"Total time male humans have spend in space",
)
# Female humans in space
data_frame_female = data_frame.loc[
data_frame["sex_or_gender"] == "female",
["birthdate", "time_in_space", "time_in_space_D"],
].copy()
create_time_of_x_in_space(
data_frame_female,
"female_humans_in_space.png",
"Total time female humans have spend in space",
)
# Humans in space
create_time_of_x_in_space(
data_frame, "humans_in_space.png", "Total time humans have spend in space"
)
# Dead and alive astronauts analysis
died_data_frame = data_frame.loc[data_frame["alive"] == 0, ["died_with_age"]].copy()
age_data_frame = data_frame.loc[data_frame["alive"] == 1, ["age"]].copy()
# Combined histogram of dead and alive astronauts
create_age_histogram(age_data_frame, died_data_frame)
# Box plots of dead vs alive astronauts
create_age_boxplot(age_data_frame, died_data_frame)
# Main entry point
if __name__ == "__main__":
perform_analysis()
from os import path, makedirs
import pandas as pd
import matplotlib.pyplot as plt
from datetime import date
def calculate_age(born):
today = date.today()
return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
def is_alive(date_of_death):
if pd.isnull(date_of_death):
return True
return False
def died_with_age(row):
if pd.isnull(row["date_of_death"]):
return None
born = row["birthdate"]
today = row["date_of_death"]
return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
plt.style.use("ggplot")
df = pd.read_json("data/astronauts.json")
df = df.rename(index=str, columns={"astronaut": "astronaut_id", "astronautLabel": "name","birthplaceLabel": "birthplace","sex_or_genderLabel": "sex_or_gender"})
df = df.set_index("astronaut_id")
df = df.dropna(subset=["time_in_space"])
df["time_in_space"] = df["time_in_space"].astype(int)
df["time_in_space"] = pd.to_timedelta(df["time_in_space"], unit="m")
df["time_in_space_D"] = df["time_in_space"].astype("timedelta64[D]")
df["birthdate"] = pd.to_datetime(df["birthdate"])
df["date_of_death"] = pd.to_datetime(df["date_of_death"])
df.sort_values("birthdate", inplace=True)
df["alive"] = df["date_of_death"].apply(is_alive)
df["age"] = df["birthdate"].apply(calculate_age)
df["died_with_age"] = df.apply(died_with_age, axis=1)
# Male humans in space
df_male = df.loc[df["sex_or_gender"] == "male", ["birthdate", "time_in_space", "time_in_space_D"]].copy()
reduced_df = df_male[["birthdate", "time_in_space", "time_in_space_D"]].copy()
reduced_df["accumulated_time_in_minutes"] = reduced_df["time_in_space"].cumsum()
reduced_df["accumulated_time_in_days"] = reduced_df["time_in_space_D"].cumsum()
reduced_df.plot(x="birthdate", y="accumulated_time_in_days")
plt.title("Total time male humans have spend in space")
plt.xlabel("Years")
plt.ylabel("t in days")
fig = plt.gcf()
fig.savefig("male_humans_in_space.png")
# Female humans in space
df_female = df.loc[df["sex_or_gender"] == "female", ["birthdate", "time_in_space", "time_in_space_D"]].copy()
reduced_df = df_female[["birthdate", "time_in_space", "time_in_space_D"]].copy()
reduced_df["accumulated_time_in_minutes"] = reduced_df["time_in_space"].cumsum()
reduced_df["accumulated_time_in_days"] = reduced_df["time_in_space_D"].cumsum()
reduced_df.plot(x="birthdate", y="accumulated_time_in_days")
plt.title("Total time female humans have spend in space")
plt.xlabel("Years")
plt.ylabel("t in days")
fig = plt.gcf()
fig.savefig("female_humans_in_space.png")
# Humans in space
reduced_df = df[["birthdate", "time_in_space", "time_in_space_D"]].copy()
reduced_df["accumulated_time_in_minutes"] = reduced_df["time_in_space"].cumsum()
reduced_df["accumulated_time_in_days"] = reduced_df["time_in_space_D"].cumsum()
reduced_df.plot(x="birthdate", y="accumulated_time_in_days")
plt.title("Total time humans have spend in space")
plt.xlabel("Years")
plt.ylabel("t in days")
fig = plt.gcf()
fig.savefig("humans_in_space.png")
died_df = df.loc[df["alive"] == 0, ["died_with_age"]].copy()
age_df = df.loc[df["alive"] == 1, ["age"]].copy()
# Combined Histogram of dead and alive astronauts
fig, axs = plt.subplots(1, 1)
axs.hist([died_df["died_with_age"], age_df["age"]], bins=70, range=(31, 100), stacked=True)
axs.set_xlabel("Age")
axs.set_ylabel("Number of astronauts")
axs.set_title("Dead vs. Alive astronauts")
fig.savefig("combined_histogram.png")
# Box plots of dead vs alive astronauts
fig, axs = plt.subplots(1, 1)
axs.boxplot([died_df["died_with_age"], age_df["age"]])
axs.set_title("Age distribution; Dead vs. Alive astronauts")
axs.set_xlabel("Category")
plt.setp(axs, xticks=[1, 2], xticklabels=["Dead", "Alive"])
axs.set_ylabel("Age")
fig.savefig("boxplot.png")
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