Week8 final

AD450_math.py

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+def add(num1, num2):
+    return num1 + num2
+
+def multiply(num1, num2):
+    return num1 * num2
+

data_processing.py

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+# This file is used by week_7
+import pandas as pd
+
+# Problem 1
+def rename_columns(df_raw):
+    """
+    Convert blank spaces to _
+    Convert everything into lower case
+    Remove trailing and leading spaces
+    Return a new DataFrame object
+
+    I noticed you need to use strip() before replace otherwise it would replace any 
+    leading or trailing spaces with a _
+    """
+    df_raw.rename(columns={"original_titlê":"original_title", "Genrë¨":"genre", "Unnamed:_8":"unnamed_8"}, inplace=True)
+    df_raw.columns = df_raw.columns.str.strip()
+    df_raw.columns = df_raw.columns.str.replace(' ', '_')
+    df_raw.columns = df_raw.columns.str.lower()
+    return df_raw
+
+# Problem #2
+def remove_fully_null_columns_rows(df_raw):
+    """
+    Remove rows that are completely null
+    Remove columns that are completely null
+    Return a new DataFrame object
+    """
+    df_raw = df_raw.dropna(how="all")
+    df_raw = df_raw.dropna(axis="columns", how="all")
+    return df_raw
+
+# Problem #3
+def clean_and_fill_content_rating(df_raw):
+    """
+    Convert all NaN values in content_rating to "Unrated"
+    Convert all "Not Rated" with "Unrated"
+    Return a new DataFrame object
+    """
+    df_raw.loc[df_raw["content_rating"].isnull(), "content_rating"] = "Unrated"
+    df_raw.loc[df_raw["content_rating"] == "Not Rated", "content_rating"] = "Unrated"
+    return df_raw
+
+# Problem #4
+def clean_release_year(df_raw):
+    """
+    Add new column "release_year_coerce", use pd.to_datetime with errors="coerce"
+    Add new column "release_year_mixed", use pd.to_datetime with errors="coerce" and format="mixed"
+    Return a new DataFrame object
+    """
+    df_raw["release_year_coerce"] = pd.to_datetime(df_raw["release_year"], errors="coerce")
+    df_raw["release_year_mixed"] = pd.to_datetime(df_raw["release_year"], errors="coerce", format="mixed", dayfirst=True)
+    return df_raw
+
+# Problem #5
+def clean_income(df_raw):
+    """
+    Remove $, commas and other issues and covert to an int
+    Return a new DataFrame object
+    """
+    df_raw["income"] = df_raw["income"].str.replace(r'[$,]','', regex=True)
+    df_raw["income"] = df_raw["income"].str.replace('o', '0', regex=False)
+    df_raw["income"] = df_raw["income"].astype("Int64")
+    return df_raw
+
+