init

python/atms-310/midterms/midterm2.py

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+import pandas as pd
+import numpy as np
+import scipy.stats as s
+
+# 1a.
+fileobj = open("./mt2_datafile.txt", "r")
+
+# 1b.
+datai = fileobj.readlines()
+datam = datai[0]
+dataf = datam.split(',')
+print(dataf)
+
+# 1c.
+dataa = np.array(dataf)
+dataa = np.reshape(dataf,(3,7))
+print(dataa)
+
+# 1d.
+for n in range(len(dataf)):
+    dataf[n] = int(dataf[n])
+print(dataf)
+
+print(np.mean(dataf))
+print(np.median(dataf))
+print(np.std(dataf))
+print(s.iqr(dataf))
+
+# mean and median are close, no outliers
+# stddev is quite small, suggesting consistent data
+# iqr is larger than stddev, so the middle is more spread out than the rest (??? perhaps ???)
+
+# 2a.
+students = {"name": ["garrus vakarian", "matilda bradbury", "cordelia vorkosigan", "kira nerys", "Jean-Luc Picard"],\
+	"credits": [37,43,36,23,50], "GPA": [3.2,4.0,3.7,3.6,3.4], "Hometown": ["Palaven", "Whitestone","Vashnoi",\
+	"Dahkur", "La Barre"]}
+
+# 2b.
+# fyi: sdt stands for "superior data type"
+sdt = pd.DataFrame(students)
+print(sdt)
+# beautiful. incredible. just amazing.
+
+# 2c.
+sdt.set_index("name",inplace=True)
+
+# 2d.
+print("kira's GPA:" + str(sdt.loc["kira nerys","GPA"]))
+
+# 2e.
+sdt.loc["matilda bradbury","credits"] = 44
+print(sdt)
+
+# 2f.
+print(sdt.credits.describe())
+print(sdt.GPA.describe())
+
+# 2g.
+
+height = [10,23,-1,43,100]
+
+sdt["height (in meters)"] = height
+print(sdt)