streamlit init

.streamlit/config.toml

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+[client]
+showSidebarNavigation = false

src/home.py

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+import streamlit as st
+from io import StringIO
+import pandas as pd
+
+def  statistics(df):
+    nan_counts = df.isnull().sum(axis=1).sum()
+
+    st.write("*Number of columns*:", len(df.columns))
+    st.write("*Number of rows*:", len(df.index))
+    
+    st.write("*Nan Counts*: ", nan_counts)
+    st.write(df.isna().sum())
+
+def display_df_first_and_lasts_lines(df):
+    fl = df.head(10)
+    ll = df.tail(10)
+    concat = pd.concat([fl, ll])
+    st.dataframe(concat)
+
+def nav_bar():
+    st.page_link("./home.py", label="Import", icon="⬆️", help=None)
+    st.page_link("pages/clean.py", label="Clean", icon="🧼", help=None)
+    st.page_link("pages/visualize.py", label="Visualize", icon="👁️", help=None)
+    st.page_link("pages/prediction.py", label="Predict", icon="🔮", help=None)
+    st.page_link("pages/evaluate.py", label="Evaluate", icon=None, help=None)
+
+def clean_dataframe(line):
+    # Call to function to clean data
+    line.empty()
+    line.write("Dataframe has been cleaned")
+
+def main():
+    nav_bar()
+    st.write("# Pow: Your data analyser")
+
+    uploaded_file = st.file_uploader("Choose a file")
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        st.session_state.orig_df = df
+        st.write("## Dataframe (10 first/last lines)")
+        display_df_first_and_lasts_lines(df)
+
+        st.write("## Statistics")
+        statistics(df)
+
+        if st.button("Next"):
+            st.switch_page("pages/clean.py")
+
+main()

src/pages/clean.py

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+import streamlit as st
+
+st.write("# 🧼 Data cleaning")
+
+st.write("## Missing data")
+rm_empty_rows_or_cols = st.checkbox("Remove empty rows or columns", True)
+
+st.write("#### Replace missing values")
+replace_methods = ["Mean","Median","Mode","KNN","Regression"]
+replace_method = st.radio('Choose an option:', replace_methods)
+
+st.write("## Normalize data")
+normalize_methods = ["Min-Max","Z-Score","Another One"]
+normalize_method = st.radio('Choose an option:', normalize_methods)
+
+if st.button("Clean dataset"):
+    # TODO: Actual processing
+    st.write("TODO")

src/pages/visualize.py

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+import streamlit as st
+import matplotlib.pyplot as plt
+
+df = st.session_state.orig_df
+df_columns = df.columns.tolist()
+
+st.write("# 📊 Visualization")
+
+st.write("## Histograms")
+hist_tabs = st.tabs(df_columns)
+
+for idx, tab in enumerate(hist_tabs):
+    tab.write("##### "+df_columns[idx])
+    tab.bar_chart(df[df_columns[idx]])
+
+st.write("## Box & Whisker")
+baw_tabs = st.tabs(df_columns)
+
+for idx, tab in enumerate(baw_tabs):
+    tab.write("##### "+df_columns[idx])
+    fig, ax = plt.subplots()
+    df[df_columns[idx]].plot(kind='box')
+    st.pyplot(fig)