Tab text

display.py

@@ -0,0 +1,26 @@
+# tab_text/display.py
+import streamlit as st
+from tab_text.logics import TextColumn
+
+def display_tab_text_content(file_path=None,df=None):
+    st.title("Text Serie Analysis")
+
+    text_column_instance = TextColumn(file_path, df)
+    text_column_instance.find_text_cols()
+
+    if not text_column_instance.cols_list:
+        st.warning("No text columns found in the dataset.")
+        return
+
+    selected_column = st.selectbox("Select a text column to explore:", text_column_instance.cols_list)
+    text_column_instance.set_data(selected_column)
+
+    with st.expander("Text Column Analysis"):
+        st.subheader("Summary:")
+        st.table(text_column_instance.get_summary())
+
+        st.subheader("Histogram:")
+        st.altair_chart(text_column_instance.barchart)
+
+        st.subheader("Top 20 Most Frequent Values:")
+        st.dataframe(text_column_instance.frequent)

logics.py

@@ -0,0 +1,114 @@
+# tab_text/logics.py
+
+import pandas as pd
+import altair as alt
+
+class TextColumn:
+    def __init__(self, file_path=None, df=None):
+        self.file_path = file_path
+        self.df = df
+        self.cols_list = []
+        self.serie = None
+        self.n_unique = None
+        self.n_missing = None
+        self.n_empty  = None
+        self.n_mode = None
+        self.n_space = None
+        self.n_lower = None
+        self.n_upper = None
+        self.n_alpha = None
+        self.n_digit = None
+        self.barchart = alt.Chart()
+        self.frequent = pd.DataFrame(columns=['value', 'occurrence', 'percentage'])
+
+    def find_text_cols(self):
+        if self.df is None and self.file_path is not None:
+            self.df = pd.read_csv(self.file_path)
+
+        if self.df is not None:
+            self.cols_list = [col for col in self.df.columns if self.df[col].dtype == 'int64']
+
+    def set_data(self, col_name):
+        self.serie = self.df[col_name] if col_name in self.df.columns else None
+        if self.is_serie_none():
+            return
+
+        self.convert_serie_to_text()
+        self.set_unique()
+        self.set_missing()
+        self.set_empty()
+        self.set_mode()
+        self.set_whitespace()
+        self.set_lowercase()
+        self.set_uppercase()
+        self.set_alphabet()
+        self.set_digit()
+        self.set_barchart(col_name)
+        self.set_frequent()
+
+    def convert_serie_to_text(self):
+        self.serie = self.serie.astype(str)
+
+    def is_serie_none(self):
+        return self.serie is None or self.serie.empty
+
+    def set_unique(self):
+        self.n_unique = self.serie.nunique()
+
+    def set_missing(self):
+        self.n_missing = self.serie.isnull().sum()
+
+    def set_empty(self):
+        self.n_empty = (self.serie == '').sum()
+
+    def set_mode(self):
+        self.n_mode = self.serie.mode().iloc[0]
+
+    def set_whitespace(self):
+        self.n_space = self.serie.apply(lambda x: x.isspace()).sum()
+
+    def set_lowercase(self):
+        self.n_lower = self.serie.str.islower().sum()
+
+    def set_uppercase(self):
+        self.n_upper = self.serie.str.isupper().sum()
+
+    def set_alphabet(self):
+        self.n_alpha = self.serie.apply(lambda x: x.isalpha()).sum()
+
+    def set_digit(self):
+        self.n_digit = self.serie.apply(lambda x: x.isdigit()).sum()
+
+    def set_barchart(self,col_name):
+        print(self.serie.reset_index())
+        chart = alt.Chart(self.serie.reset_index(), height=200).mark_bar().encode(
+            x=alt.X(col_name+':Q', title='Values', bin=True),
+            #y=alt.Y('count()', title='Count',bin=True),
+            y='count()',
+            #tooltip=['SalePrice:Q', 'count()']
+        ).interactive()
+
+        self.barchart = chart
+
+    def set_frequent(self, end=20):
+        value_counts = self.serie.value_counts().head(end).reset_index()
+        value_counts.columns = ['value', 'occurrence']
+        value_counts['percentage'] = (value_counts['occurrence'] / len(self.serie)) * 100
+
+        self.frequent = value_counts
+
+    def get_summary(self):
+        summary_data = [
+            ("Number of Unique Values", self.n_unique),
+            ("Number of Rows with Missing Values", self.n_missing),
+            ("Number of Empty Rows", self.n_empty),
+            ("Number of Rows with Only Whitespaces", self.n_space),
+            ("Number of Rows with Only Lowercases", self.n_lower),
+            ("Number of Rows with Only Uppercases", self.n_upper),
+            ("Number of  Rows with Alphabets", self.n_alpha),
+            ("Number of Rows with Numbers", self.n_digit),
+            ("Mode Value", self.n_mode),
+        ]
+
+        summary_df = pd.DataFrame(summary_data, columns=['Description', 'Value'])
+        return summary_df