Spring 19

redi/ws_19/lectures/00_preparation_session/data/.~lock.skills_after.csv#

@@ -0,0 +1 @@
+,flo,flo-x380,09.02.2019 10:12,file:///home/flo/.config/libreoffice/4;

redi/ws_19/lectures/00_preparation_session/data/.~lock.skills_before.csv#

@@ -0,0 +1 @@
+,flo,flo-x380,09.02.2019 10:12,file:///home/flo/.config/libreoffice/4;

redi/ws_19/lectures/00_preparation_session/data/skills_after.csv

@@ -0,0 +1,21 @@
+Second group,Dardeery,Levin,Priska,Uli,Celsa
+Communication Knowledge,2,3,3,4,2
+Data Visualization,3,3,3,2,2
+Reporting,4,3,0,4,2
+Data Fundamentals,1,3,3,3,3
+Database Fundamentals,2,3,2,3,3
+Domain Knowledge,3,3,0,4,4
+Project Management,2,2,1,3,2
+Budgeting,2,0,0,4,0
+Machine Learning,1,4,4,2,3
+Math & Statistics,2,4,3,2,3
+Optimization,0,3,3,1,3
+Statistical Modeling,2,3,3,2,4
+Collaboration,0,3,3,3,4
+Computer Science Fundamentals,1,3,3,2,3
+Scripting Language,1,4,4,2,4
+Statistical Computing Package,2,4,4,2,4
+Cloud Technology,0,3,1,1,2
+OS Administration,3,3,3,3,4
+Container,0,3,0,1,3
+Data & Database Administration,1,3,1,2,3

redi/ws_19/lectures/00_preparation_session/data/skills_before.csv

@@ -0,0 +1,16 @@
+Second group,Dardeery,Levin,Priska,Uli,Celsa
+Data Visualization,1,2,2,2,2
+Reporting,0,0,0,4,1
+Data Fundamentals,0,0,3,2,2
+Database Fundamentals,0,1,2,2,2
+Domain Knowledge,1,1,0,3,4
+Machine Learning,0,2,4,0,1
+Math & Statistics,2,4,3,2,2
+Optimization,0,2,3,0,3
+Statistical Modeling,0,2,2,2,1
+Collaboration,0,1,2,1,1
+Computer Science Fundamentals,0,1,3,1,0
+Statistical Computing Package,0,3,3,0,3
+Cloud Technology,0,0,1,0,1
+OS Administration,1,3,3,2,4
+Container,0,0,0,0,0

redi/ws_19/utils/plots.py

@@ -0,0 +1,48 @@
+# import required libraries
+import matplotlib.pyplot as plt
+from math import pi
+
+# define functions
+
+def spider(skills, categories):
+    my_dpi=96
+    plt.figure(figsize=(800/my_dpi, 800/my_dpi), dpi=my_dpi)
+
+    n_plots = len(skills)
+
+    # Create a color palette:
+    my_palette = plt.cm.get_cmap("Set2", n_plots)
+
+    # number of variable
+    N = len(categories)
+
+    # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
+    angles = [n / float(N) * 2 * pi for n in range(N)]
+    angles += angles[:1]
+
+    for i, row in enumerate(skills):
+        # Initialise the spider plot
+        ax = plt.subplot(n_plots + 1 // 2, min(2, n_plots), i + 1, polar=True)
+
+        # If you want the first axis to be on top:
+        ax.set_theta_offset(pi / 2)
+        ax.set_theta_direction(-1)
+
+        # Draw one axe per variable + add labels labels yet
+        plt.xticks(angles[:-1], categories, color='grey', size=7)
+
+        # Draw ylabels
+        ax.set_rlabel_position(0)
+        plt.yticks([1,2,3,4], ["1","2","3","4"], color="grey", size=7)
+        plt.ylim(0,5)
+
+        # Ind1
+        values=[row[cat] for cat in categories]
+        values += values[:1]
+        ax.plot(angles, values, color=my_palette(i), linewidth=2, linestyle='solid')
+        ax.fill(angles, values, color=my_palette(i), alpha=0.4)
+
+        # Add a title
+        plt.title(row['Title'], size=10, color=my_palette(i), y=1.1)
+
+plt.tight_layout()