Create explore_page.py

explore_page.py

@@ -0,0 +1,75 @@
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def shorten_categories(categories, cutoff):
+    categorical_map = {}
+    for i in range(len(categories)):
+        if categories.values[i] >= cutoff:
+            categorical_map[categories.index[i]] = categories.index[i]
+        else:
+            categorical_map[categories.index[i]] = 'Other'
+    return categorical_map
+
+def clean_experience(x):
+    if x ==  'More than 50 years':
+        return 50
+    if x == 'Less than 1 year':
+        return 0.5
+    return float(x)
+
+def clean_education(x):
+    if 'Bachelor’s degree' in x:
+        return 'Bachelor’s degree'
+    if 'Master’s degree' in x:
+        return 'Master’s degree'
+    if 'Professional degree' in x or 'Other doctoral' in x:
+        return 'Post grad'
+    return 'Less than a Bachelors'
+
+@st.cache_data
+def load_data():
+    df = pd.read_csv("survey_results_public.csv")
+    df = df[["Country", "EdLevel", "YearsCodePro", "Employment", "ConvertedCompYearly"]]
+    df = df.rename({"ConvertedCompYearly": "Salary"}, axis=1)
+    df = df[df["Salary"].notnull()]
+    df = df.dropna()
+    df = df[df["Employment"] == "Employed, full-time"]
+    df = df.drop("Employment", axis=1)
+
+    country_map = shorten_categories(df.Country.value_counts(), 400)
+    df['Country'] = df['Country'].map(country_map)
+    df = df[df["Salary"] <= 250000]
+    df = df[df["Salary"] >= 10000]
+    df = df[df['Country'] != 'Other']
+
+    df['YearsCodePro'] = df['YearsCodePro'].apply(clean_experience)
+    df['EdLevel'] = df['EdLevel'].apply(clean_education)
+    return df
+
+df = load_data()
+
+def show_explore_page():
+    st.title("Explore Software Engineer Salaries")
+
+    st.write("""### Stack Overflow Developer Survey 2022""")
+
+    data = df["Country"].value_counts()
+
+    fig1, ax1 = plt.subplots()
+    ax1.pie(data, labels=data.index, autopct="%1.1f%%", shadow=True, startangle=90)
+    ax1.axis("equal") # Equal aspect ratio ensures that pie is drawn as a circle.
+
+    st.write("""#### Number of Data from different countries""")
+
+    st.pyplot(fig1)
+
+    st.write("""#### Mean Salary Based On Country""")
+
+    data = df.groupby(["Country"])["Salary"].mean().sort_values(ascending=True)
+    st.bar_chart(data)
+
+    st.write("""#### Mean Salary Based On Experience""")
+
+    data = df.groupby(["YearsCodePro"])["Salary"].mean().sort_values(ascending=True)
+    st.line_chart(data)