add function documentation to util.py

src/my_package/util.py

@@ -3,6 +3,12 @@
 
 # Function to replace the norcid 'æøå'
 def replace_nordic(city_name):
+    '''
+    This function is taking in the parameter city_name. Thwn it goes to each letter to check for any 
+    of the nordic letters 'æøå'. If it is one of these letters, it will be replaced like this:
+    'æ' = 'ae', 'ø' = 'o', 'å' = 'aa'. And then the city_name will be returned.
+    '''
+
     for letter in city_name:
         if letter in 'æøå':
             city_name = city_name.replace('æ', 'ae')
@@ -12,17 +18,33 @@ def replace_nordic(city_name):
 
 # Get the city_name input, convert nordic 'æøå'
 def input_place():
+    '''
+    This function is used to get user-input for a city_name. It will first ask the user for a city_name, 
+    then runs the function to check for any nordic letters, before it returns the city_name.
+    '''
+
     city_name = input("Enter city name: ")
     city_name = replace_nordic(city_name)
     return city_name
 
 # Function to convert from kelvin to celsius temp
 def kelvin_to_celsius(temp_in_kelvin):
+    '''
+    This function is taking in the parameter temp_in_kelvin, and make a new variable temp_in_celsius.
+    The function transform from kelvin to celsius by subtracting 273.15 from the kelvin_temps.
+    '''
+
     temp_in_celsius = temp_in_kelvin - 273.15
     return temp_in_celsius
 
 # Ensure wqnted columns, fill with "NaN" if they dont exsist
 def ensure_column(df, columns):
+    '''
+    This function is taking in the parametre df and columns. It will then check if the columns are in the df.
+    If the columns are not present, it will print a statement saying it is not present, before adding the columns
+    and fill in the value NaN.
+    '''
+
     for col in columns:
         if col not in df.columns:
             print(f"'{col}' is not present in the DataFrame. Filling with NaN.")
@@ -31,6 +53,11 @@ def ensure_column(df, columns):
 
 # Fill "NaN" in wanted columns with 0
 def fill_column_0(df, columns):
+    '''
+    This function is taking in the parameters df and columns. It will then check if the columns include any NaN
+    values and fill these with 0.
+    '''
+
     for col in columns:
         try:
             df[col] = df[col].fillna(0)
@@ -40,6 +67,11 @@ def fill_column_0(df, columns):
 
 # Cleans statistic-data, drop all columns that end with '...' using the filter function
 def clean_df(df):
+    '''
+    This function is taking in the parameter df, and dataframe for statistical data.
+    It will then drop all the columns that end with '.p25', '.p75', '.st_dev' and '.num'.
+    '''
+
     df = df.drop(columns=df.filter(like='.p25').columns)
     df = df.drop(columns=df.filter(like='.p75').columns)
     df = df.drop(columns=df.filter(like='.st_dev').columns)
@@ -49,6 +81,12 @@ def clean_df(df):
 
 # Find highest and lowest temp from statistical data
 def get_records(df, city_name):
+    '''
+    This function is taking in the parameters, df and city_name from statistical data.
+    It will then make a new dataframe, with the maximum and minimum temperatur based on both
+    mean_temp and record_temp. The dataframe will be written to a new json-file with the city_name as filename.
+    '''
+
     if df.empty: 
         print("df is empty")
 
@@ -68,4 +106,12 @@ def get_records(df, city_name):
         folder = "../data/json/output_records"
         filename = f"records_{city_name}"
 
-        return summary_df, filename, folder
+        return summary_df, filename, folder
+
+print(replace_nordic.__doc__)
+print(input_place.__doc__)
+print(kelvin_to_celsius.__doc__)
+print(ensure_column.__doc__)
+print(fill_column_0.__doc__)
+print(clean_df.__doc__)
+print(get_records.__doc__)