Bump data.

python-versions.py

@@ -14,6 +14,7 @@ from pypinfo.core import build_query, create_client, create_config, parse_query_
 from pypinfo.db import get_credentials
 import matplotlib.pyplot as plt
 from matplotlib.dates import date2num
+import matplotlib.ticker as mtick
 from scipy.interpolate import make_interp_spline
 import numpy as np
 
@@ -116,11 +117,12 @@ def plot_main():
     versions = db.fetch_python_version()
     biggest_value = max(version["download_count"] for version in versions)
     for row in versions:
-        if row["download_count"] > biggest_value / 20:
-            by_version[row["python_version"]][0].append(
-                mean_date(row["start_date"], row["end_date"])
-            )
-            by_version[row["python_version"]][1].append(row["download_count"])
+        if row["download_count"] <= biggest_value / 20:
+            continue
+        mid_of_month = mean_date(row["start_date"], row["end_date"])
+        _, number_of_days = calendar.monthrange(mid_of_month.year, mid_of_month.month)
+        by_version[row["python_version"]][0].append(mid_of_month)
+        by_version[row["python_version"]][1].append(row["download_count"] / number_of_days)
     plt.style.use("tableau-colorblind10")
     plt.figure(figsize=(10, 10 * 2 / 3))
     fmt = iter(cycle(["-", "--", ":", "-."]))
@@ -135,7 +137,7 @@ def plot_main():
         smooth_y = spline(smooth_x)
         plt.plot_date(smooth_x, smooth_y, label=version, fmt=next(fmt))
     plt.xlabel("Date")
-    plt.ylabel("PyPI downloads")
+    plt.ylabel("PyPI daily downloads")
     plt.legend()
     plt.savefig("python-versions.png")
 
@@ -166,25 +168,27 @@ def plot_pct():
         versions.groupby("start_date").agg(monthly_downloads=("download_count", "sum")),
         on="start_date",
     )
-    versions["pct"] = 100 * versions.download_count / versions.monthly_downloads
-    versions["date"] = pd.to_datetime(versions.start_date) + timedelta(days=14)
+    versions["pct"] = versions.download_count / versions.monthly_downloads
+    versions["date"] = pd.to_datetime(versions.start_date).dt.to_period('M')
     versions.set_index(["Python version", "date"], inplace=True)
     to_plot = versions.pct.unstack(0, fill_value=0)
     to_plot.sort_values(
         by="Python version", ascending=False, axis=1, inplace=True, key=by_versions
     )
+    pd.options.display.float_format = '{:.2%}'.format
     print(to_plot.tail(24))
     for version in HIDE:
         del to_plot[version]
     del to_plot["Other"]
     plt.style.use("tableau-colorblind10")
-    to_plot.plot.area(
+    ax = to_plot.plot.area(
         stacked=True,
         figsize=(10, 10 * 2 / 3),
         title="% of PyPI download by Python version",
         legend="reverse",
         ylabel="%",
     )
+    ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))
     plt.savefig("python-versions-pct.png")