#!/usr/bin/env python
# coding: utf-8

# ## Geopandas!
# This notebook is to highlight how easy it is to display shapefiles in a jupyter notebook
# <br>
# Shapefiles have many files, but are still one of the most common GIS file format to be sent or requested
# <br>
# Often we just want to display them, flicking back from python and GIS can be frustrating. That is one of the reasons why command line python in a GIS is so convient
# <br>
# However sometimes we just want to show out data, perhaps in a presentation and then get on with the processing and geopandas in jupyter notebooks is perfect for that
# <br>
# I came across this blog almost a year ago, and was blown away by how simple geopandas was
# <br>
# https://agilescientific.com/blog/2017/8/10/x-lines-of-python-read-and-write-a-shapefile
# 
# <br>
# This notebook is all about viewing data. We will start by viewing 1 shapefile and then 2 as layers - as you would in a GIS
# <br>
# <br>
# Data used in this notebook is a sample adapted from <br>
# https://data.gov.uk/dataset/d43263ba-6f09-458e-995e-69720909365f/crop-map-of-england-crome-2016-south-west
# <br>
# and a layer I created myself as part of my geospatial python course 
# <br>
# http://www.acgeospatial.co.uk/training/
# 
# #### Start by reading in a shapefile

# In[1]:


import geopandas as gpd
gdf = gpd.read_file('.../RPA_hexagons.shp')
print (gdf)


# ### 3 lines of code
# <br>
# Some examples in org are here
# <br>
# https://pcjericks.github.io/py-gdalogr-cookbook/vector_layers.html#get-shapefile-feature-count
# <br>
# geopandas seems easy to read for a beginner and it is fast
# <br>
# <br>
# What about viewing the data in the notebook.
# <br>
# that is just one line of code

# In[3]:


get_ipython().run_line_magic('matplotlib', 'inline')
gdf.plot()


# ### What about colouring the data based on a field 
# <br>
# (look at the table in the first cell for the field names)

# In[4]:


gdf.plot(column='LUCODE', cmap=None)


# ### What about a legend?
# <br>
# parse legend=True

# In[8]:


gdf.plot(column='LUCODE', cmap=None, legend=True)


# ### Legend is too big! Too many attributes?
# <br>
# make the figure larger
# <br>
# use figsize

# In[5]:


gdf.plot(column='LUCODE', cmap=None, legend=True, figsize=(20, 20))



# ### Add another shapefile into another geopandas dataframe

# In[7]:


gdf2 = gpd.read_file('.../Boundaries.shp')
print (gdf2)


# ### Plot them on each other and shade the new shapefile by the column 'id'

# In[11]:


import matplotlib.pyplot as plt
f, ax = plt.subplots(1)
gdf.plot(ax=ax)
gdf2.plot(ax=ax,column='id',cmap=None,)
plt.show()


# In[12]:


print (f)


# In[13]:


print (ax)


# In[ ]: