changed dependencies handling

requirements.txt

-pandas==1.0.5
-matplotlib==3.2.2
\ No newline at end of file
+pandas == 1.0.5
+matplotlib == 3.2.2
\ No newline at end of file

src/astronauts_analysis.py

@@ -7,10 +7,10 @@ from datetime import date
 from os import makedirs
 from pathlib import Path
 
-import matplotlib.pyplot as plt
-import pandas as pd
+import matplotlib.pyplot
+import pandas
 
-plt.style.use("ggplot")
+matplotlib.pyplot.style.use("ggplot")
 _ASTRONAUT_DATA = "data/astronauts.json"
 _OUTPUT_PATH = "results"
 
@@ -18,7 +18,7 @@ _OUTPUT_PATH = "results"
 ##
 # Data preparation functions
 ##
-def prepare_data_set(data_frame: pd.DataFrame) -> pd.DataFrame:
+def prepare_data_set(data_frame: pandas.DataFrame) -> pandas.DataFrame:
     """
     Prepares the raw data by:
         - dropping NaN's
@@ -37,9 +37,9 @@ def prepare_data_set(data_frame: pd.DataFrame) -> pd.DataFrame:
     # 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["time_in_space"] = pandas.to_timedelta(data_frame["time_in_space"], unit="m")
+    data_frame["birthdate"] = pandas.to_datetime(data_frame["birthdate"])
+    data_frame["date_of_death"] = pandas.to_datetime(data_frame["date_of_death"])
     data_frame.sort_values("birthdate", inplace=True)
 
     # Calculate extra columns from the original data
@@ -76,12 +76,12 @@ def is_alive(date_of_death) -> bool:
     Returns:
         bool
     """
-    if pd.isnull(date_of_death):
+    if pandas.isnull(date_of_death):
         return True
     return False
 
 
-def calculate_age(born: pd.Timestamp) -> int:
+def calculate_age(born: pandas.Timestamp) -> int:
     """
     Calculates an age from a date.
 
@@ -92,14 +92,14 @@ def calculate_age(born: pd.Timestamp) -> int:
         int
     """
 
-    if not isinstance(born, pd.Timestamp):
-        raise TypeError(f'expected {pd.Timestamp}, got {type(born)}')
+    if not isinstance(born, pandas.Timestamp):
+        raise TypeError(f'expected {pandas.Timestamp}, got {type(born)}')
 
     today = date.today()
     return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
 
 
-def died_with_age(row: pd.Series):
+def died_with_age(row: pandas.Series):
     """
     Calculates an age from a birthdate and date_of_death.
 
@@ -109,7 +109,7 @@ def died_with_age(row: pd.Series):
     Returns:
         int
     """
-    if pd.isnull(row["date_of_death"]):
+    if pandas.isnull(row["date_of_death"]):
         return None
     born = row["birthdate"]
     today = row["date_of_death"]
@@ -141,7 +141,7 @@ def create_age_histogram(age_data_frame, died_data_frame):
     in the categories 'age at dead' and 'age alive'.
     """
 
-    fig, axs = plt.subplots(1, 1)
+    fig, axs = matplotlib.pyplot.subplots(1, 1)
     axs.hist(
         [died_data_frame["died_with_age"], age_data_frame["age"]],
         bins=70,
@@ -160,16 +160,16 @@ def create_age_boxplot(age_data_frame, died_data_frame):
     in the categories dead and alive.
     """
 
-    fig, axs = plt.subplots(1, 1)
+    fig, axs = matplotlib.pyplot.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"])
+    matplotlib.pyplot.setp(axs, xticks=[1, 2], xticklabels=["Dead", "Alive"])
     axs.set_ylabel("Age")
     save(fig, "boxplot.png")
 
 
-def save(fig: plt.Figure, filename: str):
+def save(fig: matplotlib.pyplot.Figure, filename: str):
     """
     Saves a matplotlib Figure to a file. It overwrites existing files with the same filename.
 
@@ -185,7 +185,7 @@ def perform_analysis():
 
     # Set up directory structure and preprocess data
     makedirs(_OUTPUT_PATH, exist_ok=True)
-    data_frame = pd.read_json(Path(_ASTRONAUT_DATA).resolve())
+    data_frame = pandas.read_json(Path(_ASTRONAUT_DATA).resolve())
     data_frame = prepare_data_set(data_frame)
 
     # Male humans in space