Some more files

helper.py

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+import numpy as np
+
+
+def medal_tally(df):
+    medal_tally = df.drop_duplicates(
+        subset=["Team", "NOC", "Games", "Year", "City", "Sport", "Event", "Medal"]
+    )
+
+    medal_tally = (
+        medal_tally.groupby("region")
+        .sum()[["Gold", "Silver", "Bronze"]]
+        .sort_values("Gold", ascending=False)
+        .reset_index()
+    )
+
+    medal_tally["total"] = (
+        medal_tally["Gold"] + medal_tally["Silver"] + medal_tally["Bronze"]
+    )
+
+    medal_tally["Gold"].astype("int")
+    medal_tally["Silver"].astype("int")
+    medal_tally["Bronze"].astype("int")
+    medal_tally["total"].astype("int")
+
+    return medal_tally
+
+
+def country_year_list(df):
+    years = df["Year"].unique().tolist()
+    years.sort()
+    years.insert(0, "Overall")
+
+    country = np.unique(df["region"].dropna().values).tolist()
+    country.sort()
+    country.insert(0, "Overall")
+
+    return years, country
+
+
+# For plotting a line plot in the app
+
+
+# def participating_nations_over_time(df):
+def data_over_time(df, col):
+    nations_over_time = (
+        df.drop_duplicates(["Year", col])["Year"]
+        .value_counts()
+        .reset_index()
+        .sort_values("Year")
+    )
+    nations_over_time.rename(
+        columns={"count": col, "Year": "Year/Edition"}, inplace=True
+    )
+    return nations_over_time
+
+
+# Table of the most decorated/successful athletes i.e., athletes with most medal wins
+def most_successful(df, sport):
+    # Done since many values in Medal column are NaN values
+    temp_df = df.dropna(subset=["Medal"])
+    if sport != "Overall":
+        temp_df = temp_df[temp_df["Sport"] == sport]
+    # Becomes/converts to a dataframe when reset_index() is used.
+    x = (
+        temp_df["Name"]
+        .value_counts()
+        .reset_index()
+        .head(15)
+        .merge(df, left_on="Name", right_on="Name", how="left")[
+            ["Name", "count", "Sport", "region"]
+        ]
+        .drop_duplicates("Name")
+    )
+    x.rename(columns={"Name": "Name", "count": "Medals"}, inplace=True)
+    return x
+
+
+# Creating a function having inputs year and country, and will show the output on the app after the
+# selected inputs from dropdown box on the app.
+
+
+def fetch_medal_tally(df, year, country):
+    medal_df = df.drop_duplicates(
+        subset=["Team", "NOC", "Games", "Year", "City", "Sport", "Event", "Medal"]
+    )
+    # Flag set for showing year-wise medals for a specific country
+    flag = 0
+    if year == "Overall" and country == "Overall":
+        temp_df = medal_df
+    if year == "Overall" and country != "Overall":
+        flag = 1
+        temp_df = medal_df[medal_df["region"] == country]
+    if year != "Overall" and country == "Overall":
+        temp_df = medal_df[medal_df["Year"] == int(year)]
+    if year != "Overall" and country != "Overall":
+        temp_df = medal_df[
+            (medal_df["Year"] == int(year)) & (medal_df["region"] == country)
+        ]
+
+    if flag == 1:
+        x = (
+            temp_df.groupby("Year")
+            .sum()[["Gold", "Silver", "Bronze"]]
+            .sort_values("Year", ascending=True)
+            .reset_index()
+        )
+    else:
+        x = (
+            temp_df.groupby("region")
+            .sum()[["Gold", "Silver", "Bronze"]]
+            .sort_values("Gold", ascending=False)
+            .reset_index()
+        )
+
+    x["total"] = x["Gold"] + x["Silver"] + x["Bronze"]
+
+    x["Gold"].astype("int")
+    x["Silver"].astype("int")
+    x["Bronze"].astype("int")
+    x["total"].astype("int")
+
+    return x
+
+
+def yearwise_medal_tally(df, country):
+    temp_df = df.dropna(subset="Medal")
+    # Solving team sports problem
+    temp_df.drop_duplicates(
+        subset=["Team", "NOC", "Games", "Year", "City", "Sport", "Event", "Medal"],
+        inplace=True,
+    )
+
+    new_df = temp_df[temp_df["region"] == country]
+    final_df = new_df.groupby("Year").count()["Medal"].reset_index()
+
+    return final_df
+
+
+def country_event_heatmap(df, country):
+    temp_df = df.dropna(subset="Medal")
+    # Solving team sports problem
+    temp_df.drop_duplicates(
+        subset=["Team", "NOC", "Games", "Year", "City", "Sport", "Event", "Medal"],
+        inplace=True,
+    )
+
+    new_df = temp_df[temp_df["region"] == country]
+
+    pt = new_df.pivot_table(
+        index="Sport", columns="Year", values="Medal", aggfunc="count"
+    ).fillna(0)
+
+    return pt
+
+
+def most_successful_countrywise(df, country):
+    # Done since many values in Medal column are NaN values
+    temp_df = df.dropna(subset=["Medal"])
+
+    temp_df = temp_df[temp_df["region"] == country]
+    # Becomes/converts to a dataframe when reset_index() is used.
+    x = (
+        temp_df["Name"]
+        .value_counts()
+        .reset_index()
+        .head(10)
+        .merge(df, left_on="Name", right_on="Name", how="left")[
+            ["Name", "count", "Sport"]
+        ]
+        .drop_duplicates("Name")
+    )
+    x.rename(columns={"Name": "Name", "count": "Medals"}, inplace=True)
+    return x
+
+
+def weight_v_height(df, sport):
+    # Creating athlete_df dataframe
+    athlete_df = df.drop_duplicates(subset=["Name", "region"])
+    # Cleaning sorta the dataframe
+    athlete_df.fillna({"Medal": "No Medal"}, inplace=True)
+
+    if sport != "Overall":
+        temp_df = athlete_df[athlete_df["Sport"] == sport]
+        return temp_df
+    else:
+        return athlete_df
+
+
+def men_vs_women(df):
+    athlete_df = df.drop_duplicates(subset=["Name", "region"])
+
+    # Plot of men vs women participation over the years in the Olympics
+    men = (
+        athlete_df[athlete_df["Sex"] == "M"]
+        .groupby("Year")
+        .count()["Name"]
+        .reset_index()
+    )
+    women = (
+        athlete_df[athlete_df["Sex"] == "F"]
+        .groupby("Year")
+        .count()["Name"]
+        .reset_index()
+    )
+
+    final = men.merge(women, on="Year", how="left")
+    final.rename(columns={"Name_x": "Male", "Name_y": "Female"}, inplace=True)
+
+    final.fillna(0, inplace=True)
+
+    return final