Portmann Submission Ex09

Ex09pt1.py

@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov  4 17:01:10 2018
+
+Exercise09
+
+@author: Patricia
+"""
+import pandas
+import matplotlib.pyplot as plt
+import numpy as np
+
+# data loaded into a data frame
+data=pandas.read_csv("incomevSAT.txt", sep="\t")
+
+x = data.iloc[:,0] # sets x equal to income column
+yR = data.iloc[:,1] # gets SAT Reading scores
+yM = data.iloc[:,2] # gets SAT Math scores
+yW = data.iloc[:,3] # gets SAT Writing scores
+yC = yR + yM + yW # cumulative SAT
+
+plt.scatter(x, yC)
+plt.title('Family Income vs. SAT')
+plt.xlabel('Family Income') # x axis label
+plt.ylabel('SAT score') # y axis label
+z = np.polyfit(x,yC,1) # finds the linear line of best fit
+p = np.poly1d(z)
+plt.plot(x,p(x),"r--")
+plt.show()
+

Ex09pt2.py

@@ -0,0 +1,67 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov  4 20:26:13 2018
+
+Exercise 09 part 2
+
+@author: Patricia
+"""
+
+import pandas
+import matplotlib.pyplot as plt
+import numpy as np
+from plotnine import *
+
+# data loaded into a data frame
+data=pandas.read_csv("data.txt", sep=",")
+
+# pt 1: make barplot with mean of the four populations using matplotlib
+north = 0
+north_count = 0
+south = 0
+south_count = 0
+east = 0
+east_count = 0
+west = 0
+west_count = 0
+region = 'none'
+# iterate through data frame to get sum of north,south,east,west and their # observations
+for x in range(0,len(data)):
+    region = str(data.iloc[x,0])
+    if region == 'north':
+        north = north + int(data.iloc[x,1])
+        north_count = north_count + 1
+    elif region == 'south':
+        south = south + int(data.iloc[x,1])
+        south_count = south_count + 1
+    elif region == 'east': 
+        east = east + int(data.iloc[x,1])
+        east_count = east_count + 1
+    elif region == 'west':
+        west = west + int(data.iloc[x,1])
+        west_count = west_count + 1
+# find mean of each area using sum and count
+means = [north/north_count, south/south_count, east/east_count, west/west_count]
+
+opacity = 0.8 # a color strength I liked
+n_groups = 4 # 4 bars
+fig, ax = plt.subplots() # bar graph is named ax
+
+index = np.arange(n_groups)
+bar_width = 0.35 # width of bar
+
+rects = ax.bar(index, means, bar_width, alpha = opacity, color='g') #graph
+
+ax.set_xlabel('Region')#x axis label
+ax.set_ylabel('Mean')# y axis label
+ax.set_title('Mean Observations per Region')# title
+ax.set_xticks(index) # middle of each bar
+ax.set_xticklabels(('North', 'South', 'East', 'West'))# labels to x axis
+fig.tight_layout()# nice layout
+plt.show() #display
+
+# part 2: a scatterplot of the data using plotnine
+scatter = ggplot(data,aes(x="region",y="observations"))
+print(scatter+geom_point(alpha=0.1)+theme_classic())
+
+

incomevSAT.txt

@@ -0,0 +1,11 @@
+Family Income	Critical Reading	Math	Writing
+20000	433	461	428
+40000	463	481	453
+60000	485	500	473
+80000	499	512	486
+100000	511	525	499
+120000	523	539	512
+140000	527	543	517
+160000	534	551	525
+200000	540	557	534
+300000	567	589	566