Add VT2-examples: all 6 chapters of 'Visualize This' (110 files) — data scraping, ETL, trends, categorical, multivariate, spatial, distribution analysis

VT2-examples/vt2-ch5-codedata-dir/make-symbols-chart.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+# import seaborn as sns
+# import math
+
+# Load data
+marjobs = pd.read_csv("data/physician-marry.tsv", delimiter="\t")
+marsorted = marjobs.sort_values(by=['p'], 
+    ascending=False)[0:10] # Top 10.
+
+# Prepare data for circles using bubble plot
+mardf = pd.DataFrame({
+    "x": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+    "y": [10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
+    "r": list(marsorted.p * 50000)
+})
+
+# sns.set(rc={'pdf.fonttype': 42 })
+
+# p1 = sns.relplot(x="x", y="y", size="r", sizes=(40,2000), data=mardf)
+# fig = p1.get_figure()
+# p1.savefig("symbols-chart-mar.pdf")
+
+
+# Figure size
+plt.rcParams['figure.figsize'] = (7, 10)
+plt.rcParams['pdf.fonttype'] = 42
+
+# Plot
+plt.scatter("x", "y", s="r", data=mardf)
+
+# Text
+for i in range(0, len(marsorted)):
+    thelabel = str(round(100*marsorted.iloc[i].p)) + "%, "
+    thelabel += marsorted.iloc[i].occname
+    plt.text(1, 10-i, thelabel)
+
+# Save as PDF
+plt.savefig("symbols-chart-mar.pdf")