Upload functions.py

functions.py

@@ -0,0 +1,63 @@
+import io
+import pandas as pd
+import streamlit as st
+
+def df_info(df):
+    df.columns = df.columns.str.replace(' ', '_')
+    buffer = io.StringIO() 
+    df.info(buf=buffer)
+    s = buffer.getvalue() 
+
+    df_info = s.split('\n')
+
+    counts = []
+    names = []
+    nn_count = []
+    dtype = []
+    for i in range(5, len(df_info)-3):
+        line = df_info[i].split()
+        counts.append(line[0])
+        names.append(line[1])
+        nn_count.append(line[2])
+        dtype.append(line[4])
+
+    df_info_dataframe = pd.DataFrame(data = {'#':counts, 'Column':names, 'Non-Null Count':nn_count, 'Data Type':dtype})
+    return df_info_dataframe.drop('#', axis = 1)
+
+def df_isnull(df):
+    res = pd.DataFrame(df.isnull().sum()).reset_index()
+    res['Percentage'] = round(res[0] / df.shape[0] * 100, 2)
+    res['Percentage'] = res['Percentage'].astype(str) + '%'
+    return res.rename(columns = {'index':'Column', 0:'Number of null values'})
+
+def number_of_outliers(df):
+    
+    df = df.select_dtypes(exclude = 'object')
+    
+    Q1 = df.quantile(0.25)
+    Q3 = df.quantile(0.75)
+    IQR = Q3 - Q1
+    
+    ans = ((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).sum()
+    df = pd.DataFrame(ans).reset_index().rename(columns = {'index':'column', 0:'count_of_outliers'})
+    return df
+
+def space(num_lines=1):
+    for _ in range(num_lines):
+        st.write("")
+
+def sidebar_space(num_lines=1):
+    for _ in range(num_lines):
+        st.sidebar.write("")
+
+
+def sidebar_multiselect_container(massage, arr, key):
+    
+    container = st.sidebar.container()
+    select_all_button = st.sidebar.checkbox("Select all for " + key + " plots")
+    if select_all_button:
+        selected_num_cols = container.multiselect(massage, arr, default = list(arr))
+    else:
+        selected_num_cols = container.multiselect(massage, arr, default = arr[0])
+
+    return selected_num_cols