Commit 44d1fc8b authored by Robert Behling's avatar Robert Behling
Browse files

cleaned code

parent f7fc09c5
from os import path, makedirs
import pandas as pd
import matplotlib.pyplot as plt
"""
module docstring: description of what the .py file (module) does. here comes the module docstring:
This script analyses a data set about astronauts and creates plots as a result.
"""
#1. import standard imports
from datetime import date
from pathlib import Path
from os import makedirs
#2. import additional third party imports (not included in python)
import matplotlib.pyplot as plt
import pandas as pd
#3. import local files/libraries
#within these classes import it alphabetically!
plt.style.use("ggplot")
_ASTRONAUT_DATA = "data/astronauts.json"
_OUTPUT_PATH = "results"
def calculate_age(born):
def calculate_age(born: pd.Timestamp) -> int:
"""
Calculates an age from a date.
:param born: pandas.Timestamp
:return: years as integer
"""
today = date.today()
return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
def is_alive(date_of_death):
def is_alive(date_of_death) -> bool: # the type is not defined, because it can be pd.Timesrtamp AND Null
"""
Checks if 'date_of_death' exists or not
:param date_of_death: pandas.Timestamp or NaTType
:return: bool
"""
if pd.isnull(date_of_death):
return True
return False
def died_with_age(row):
def died_with_age(row: pd.Series): # the output type is not defined, because it can be pd.Timestamp AND Null
"""
Calculates age from birthdate and date_of_death
:param row: pandas.Series with the columns ´birthdate' and ´date_of_death'
:return: int, if person died, none otherwise
"""
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")
# 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
##
# 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()
#def save(fig: plt.Figure, filename: str) -> None:
#"""
#Saves a matplotlib Figure to a file. It overwrites existing files.
#:param fig: matplotlib.pyplot.Figure
#:param filename: str
#"""
#fig.savefig(Path(_OUTPUT_PATH).resolve() / Path(filename)) # similar to os.pat.join()
#df = pd.read_json(_ASTRONAUT_DATA)
#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()
#save(fig, "male_humans_in_space.png")
#fig.savefig()
## 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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