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

VT2-examples/vt2-ch4-codedata-dir/make-heatmap.py

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+import seaborn as sns
+import pandas as pd
+import matplotlib.colors as mc
+
+# For defining color map
+def NonLinCdict(steps, hexcol_array):
+    cdict = {'red': (), 'green': (), 'blue': ()}
+    for s, hexcol in zip(steps, hexcol_array):
+        rgb = mc.hex2color(hexcol)
+        cdict['red'] = cdict['red'] + ((s, rgb[0], rgb[0]),)
+        cdict['green'] = cdict['green'] + ((s, rgb[1], rgb[1]),)
+        cdict['blue'] = cdict['blue'] + ((s, rgb[2], rgb[2]),)
+    return cdict
+
+# Load data
+thedata = pd.read_csv("data/month_hour_pct_matrix.csv")
+mns = thedata.iloc[0:12, 0]
+pmat = thedata.iloc[0:12, 1:25]
+
+# Set figure size
+sns.set(rc={'figure.figsize':(11,4.5), 'pdf.fonttype': 42})
+
+# Make color map
+# hc = ['#dff5ee', '#92dee9', '#3fc1f9', '#5598fd', '#a75bd8', '#d4007f']
+hc = ['#f0f0f0', '#c3dde3', '#acc6cc', '#aeaaaa', '#c0877c', '#da5241']
+th = [0, 0.1, 0.2, 0.3, .4, 1]
+cdict = NonLinCdict(th, hc)
+cm = mc.LinearSegmentedColormap('custom', cdict)
+
+# Heatmap
+p1 = sns.heatmap(
+    pmat,
+    square=True,
+    linewidths=0.1,
+    yticklabels=mns,
+    cmap=cm)
+p1.set_xticks([0.5,6.5,12.5,18.5,23.5])
+p1.set_xticklabels(["12am", "6am", "noon", "6pm", "11pm"])
+fig = p1.get_figure()
+
+# Save file
+fig.savefig("pmat-heatmap.pdf")