feat: new col1 and 2

pages/1_📈_Plotting.py

@@ -2,8 +2,8 @@ import streamlit as st
 import pandas as pd
 
 st. set_page_config(layout="wide")
-st.header("Plotting Time-Series Data")
-st.markdown("Users can load their time-series data in **.csv** format and select a particular feature and plot-type to visualize it.\
+st.header("Plotting Time Series Data")
+st.markdown("Users can load their time-series data in **.csv** format and select a particular feature and plot-type.\
  Go ahead and use the sidebar on the left to upload your data files and to start visualizing it!")
 
 
@@ -45,12 +45,14 @@ else:
 
 df_base = st.session_state.df if 'df' in list(st.session_state.keys()) else pd.DataFrame()
 n = len(df_base)
-if n:
-    st.info(f"Your data has {n} samples.")
-    slider_range = list(range(n))
-    n_plot = st.slider("How many samples do you want to plot in the same graph", slider_range[0]+1, slider_range[-1]+1, 5)
-    st.write(f"Action: {plot} using {n_plot} samples")
-    st.button("Plot now! 📊")
+col1, col2 = st.columns(2)
+with col1:
+    if n:
+        st.info(f"Your data has {n} samples.")
+        slider_range = list(range(n))
+        n_plot = st.slider("How many samples do you want to plot in the same graph", slider_range[0]+1, slider_range[-1]+1, 5)
+        st.write(f"Action: {plot} using {n_plot} samples")
+        st.button("Plot now! 📊")
 
 if not df_base.empty:
     st.warning("Consider running outlier detection to clean your data!", icon="⚠️")