notebooks; demo

Author: glamp

README.md

@@ -8,7 +8,8 @@ that allow users to quickly and easily make good looking plots. So say good-bye
 to matplotlib, and hello to ggplot as your everyday Python plotting library!
 
 # Outline
-## Intro
+## Slides (20 mins)
+### Intro
 #### Me
 - Hi, I'm Greg!
 #### What is `ggplot`?
@@ -20,7 +21,8 @@ to matplotlib, and hello to ggplot as your everyday Python plotting library!
 #### Where it's going
 - things that don't exist yet
 
-## Tutorial
+## IPython Notebook (40-50 mins)
+### Tutorial
 #### `aesthetics`
 - assigning `x` and `y`
 #### components

faceting.py

@@ -0,0 +1,36 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+N = 100
+industry = ['a','b','c']
+city = ['x','y','z']
+ind = np.random.choice(industry, N)
+cty = np.random.choice(city, N)
+jobs = np.random.randint(low=1,high=250,size=N)
+df_city =pd.DataFrame({'industry':ind,'city':cty,'jobs':jobs})
+
+## how many panels do we need?
+cols =df_city.city.value_counts().shape[0]
+fig, axes = plt.subplots(1, cols, figsize=(8, 8))
+
+for x, city in enumerate(df_city.city.value_counts().index.values):
+    data = df_city[(df_city['city'] == city)]
+    data = data.groupby(['industry']).jobs.sum()
+    print (data)
+    print type(data.index)
+    left=  [k[0] for k in enumerate(data)]
+    right=  [k[1] for k in enumerate(data)]
+
+    axes[x].bar(left,right,label="%s" % (city))
+    axes[x].set_xticks(left, minor=False)
+    axes[x].set_xticklabels(data.index.values)
+
+    axes[x].legend(loc='best')
+    axes[x].grid(True)
+    fig.suptitle('Employment By Industry By City', fontsize=20)
+plt.show(1)
+
+from ggplot import *
+
+print ggplot(aes(x='city'), data=df_city) + geom_bar() + facet_grid(x=None, y="city", nrow=1)