Create file.py

Author: deepdk

Environmental/file.py

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+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import matplotlib.pyplot as plt # for plotting the data 
+import seaborn as sns # Advanced data plotting on top of matplotlib
+import os
+from pathlib import Path
+import datatable as dt
+import plotly.express as px
+
+%matplotlib inline
+from plotly.offline import init_notebook_mode, iplot 
+import plotly.graph_objs as go
+import plotly.offline as py
+py.init_notebook_mode(connected=True)
+from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator, ImageColorGenerator
+%matplotlib inline
+
+f_area = pd.read_csv("../input/global-environmental-indicators/Forests/Forest Area.csv")
+
+f_area = f_area.drop(f_area.index[:1])
+
+f_area["%change"] = ((f_area["Forest Area, 2020 (1000 ha)"] - f_area["Forest Area, 1990 (1000 ha)"])/ f_area["Forest Area, 1990 (1000 ha)"])*100
+
+plt.figure(figsize = (20,20))
+fig = go.Figure(data=go.Choropleth(
+    locations=f_area['Country and Area'], # Spatial coordinates
+    z = f_area['%change'].astype(float), # Data to be color-coded
+    locationmode = 'country names', # set of locations match entries in `locations`
+    colorscale = 'RdYlGn',
+    colorbar_title = "%change",
+    reversescale=True
+))
+title = '<b>Forest Area % Change</b><br><sup>1990 vs 2020</sup>'
+
+fig.update_layout(
+    template="plotly_white",
+    title = {'text' : title, 
+                            'x':0.5, 'xanchor': 'center'}, 
+                   font = {"color" : 'black'}
+)
+
+fig.show()