text: # Imports from 3rd party libraries
  import dash
  import dash_bootstrap_components as dbc
  import dash_core_components as dcc
  import dash_html_components as html
  from dash.dependencies import Input, Output
  
  # Imports from this application
  from app import app
  
  # 1 column layout
  # https://dash-bootstrap-components.opensource.faculty.ai/l/components/layout
  column1 = dbc.Col(
      [
          dcc.Markdown(
              """
          
              ## **Insights**
  
              ##### Import libraries first
              ```
              import numpy as np
              import pandas as pd
              import plotly as py
              import plotly.express as px
              import plotly.graph_objs as go
              from plotly.subplots import make_subplots
              from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
              ```
              ##### load the data
              you can download the original data from [here](http://google.com)
              ```
              df = pd.read_csv('../assets/GlobalLandTemperaturesByCountry.csv')
              ```
              ##### Look at the data, find the Null values, clean the data
              ```
              print(df.shape)
              df.head()
              df.tail()
              df.isnull().sum()
              df = df.drop('AverageTemperatureUncertainty', axis=1)
              df = df.rename(columns={'dt': 'Date', 'AverageTemperature': 'Average_Temp'})
              df.isnull().sum()
              df = df.dropna()
              print(df.shape)
              df.head()
              ```
              ##### Creating Celsius to Farenheit function
              ```
              def convert_to_farenheit (c):
                  return (c*9/5)+32
              ```
              ##### Round up the result to 2 decimals as well
              ```
              df['Average_Temp_Farenheit'] = df['Average_Temp_Celsius'].apply(convert_to_farenheit).round(2)
              df['Average_Temp_Celsius'] = df['Average_Temp_Celsius'].round(2)
              ```
              ##### Group the dataframe by Country name and dataset
              ```
              df_sorted = df.groupby(['Country', 'Date']).sum().reset_index().sort_values(['Date'], ascending=False)
              df_sorted.head()
              ```
              ##### Create the data range
              ```
              start_date = '2009-12-01'
              end_date = '2013-01-01'
              mask = (df_sorted['Date'] > start_date) & (df_sorted['Date'] <= end_date)
              ```
              ##### Create a new sorted dataset
              ```
              df_2000 = df_sorted[mask]
              print(df_2000.shape)
              df_2000.head()
              ```
  
              ##### Create a dataset for Climate change by timeline
  
              ```
              df_2000_by_month = df_2000.groupby(['Date','Country']).sum().reset_index()
              df_2000_by_month.head()
              ```
              ##### Visualization
              ```
              fig = px.choropleth(df_2000_by_month, locations='Country', locationmode='country names', 
                                  color='Average_Temp_Farenheit', hover_data=['Average_Temp_Celsius'],
                                  hover_name='Country', animation_frame='Date')
  
              fig.update_layout(title_text='Average Temperature Change from 2010-01-01 to 2013-01-01', title_x = 0.5, 
                                geo=dict(showframe = False, showcoastlines = False))
              fig.show()
              ```
              You can find the GitHub page with all the code by pressing the GitHub icon below.
  
              """
          ),
  
      ],
  )
  
  layout = dbc.Row([column1])