Also plot total downloads.

README.md

@@ -1,5 +1,18 @@
 # Analysis of version adoptions on PyPI
 
+## pypi.org downloads by version
+
+![](python-versions-lines.png)
+![](python-versions-stacked.png)
+
+
+## pypi.org downloads total
+
+![](pypi-download-counts.png)
+
+
+## Where does the data come from?
+
 We get
 [publicly available PyPI download statistics](https://bigquery.cloud.google.com/table/bigquery-public-data:pypi.downloads)
 on Google BigQuery using [pypinfo](https://github.com/ofek/pypinfo/).
@@ -20,15 +33,3 @@ Then there's two main invocations, first fetch the data using:
 Then plot it using:
 
     python python-versions.py
-
-
-## Data
-
-### Percentage of pypi.org downloads
-
-![](python-versions-pct.png)
-
-
-### Number of pypi.org downloads
-
-![](python-versions.png)

python-versions.py

@@ -107,45 +107,10 @@ def fetch_main():
                 )
 
 
-def mean_date(a: datetime, b: datetime) -> datetime:
-    return a + (b - a) / 2
-
-
-def plot_main():
-    db = DB()
-    by_version = defaultdict(lambda: [[], []])
-    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:
-            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(["-", "--", ":", "-."]))
-    for version, (x, y) in by_version.items():
-        if version is None:
-            continue
-        if len(x) <= 2:
-            plt.plot(x, y, label=version)
-            continue
-        smooth_x = np.linspace(date2num(min(x)), date2num(max(x)), 200)
-        spline = make_interp_spline([date2num(d) for d in x], y, k=2)
-        smooth_y = spline(smooth_x)
-        plt.plot_date(smooth_x, smooth_y, label=version, fmt=next(fmt))
-    plt.xlabel("Date")
-    plt.ylabel("PyPI daily downloads")
-    plt.legend()
-    plt.savefig("python-versions.png")
-
-
 HIDE = {"1.17", "2.4", "2.5", "2.6", "3.2", "3.3", "3.4"}
 
 
-def plot_pct():
+def plot():
     def by_version(version_string):
         try:
             minor, major = version_string.split(".")
@@ -164,24 +129,29 @@ def plot_pct():
     )
     versions["download_count"] = pd.to_numeric(versions["download_count"])
     versions["Python version"].fillna("Other", inplace=True)
-    versions = versions.merge(
+    download_counts = versions.groupby("start_date").agg(
-        versions.groupby("start_date").agg(monthly_downloads=("download_count", "sum")),
+        monthly_downloads=("download_count", "sum")
-        on="start_date",
     )
+    plot_download_counts(download_counts)
+    versions = versions.merge(download_counts, on="start_date")
     versions["pct"] = versions.download_count / versions.monthly_downloads
-    versions["date"] = pd.to_datetime(versions.start_date).dt.to_period('M')
+    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
+    pd.options.display.float_format = "{:.2%}".format
     pd.options.display.max_rows = 999
     print(to_plot)
     for version in HIDE:
         del to_plot[version]
     del to_plot["Other"]
-    plt.style.use("tableau-colorblind10")
+    plot_lines(to_plot)
+    plot_stacked(to_plot)
+
+
+def plot_stacked(to_plot):
     ax = to_plot.plot.area(
         stacked=True,
         figsize=(10, 10 * 2 / 3),
@@ -190,7 +160,28 @@ def plot_pct():
         ylabel="%",
     )
     ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))
-    plt.savefig("python-versions-pct.png")
+    plt.savefig("python-versions-stacked.png")
+
+
+def plot_lines(to_plot):
+    ax = to_plot.plot(
+        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-lines.png")
+
+
+def plot_download_counts(to_plot):
+    ax = to_plot.plot(
+        figsize=(10, 10 * 2 / 3),
+        title="PyPI number of downloads",
+        legend="reverse",
+        xlabel="date",
+    )
+    plt.savefig("pypi-download-counts.png")
 
 
 def parse_args():
@@ -207,5 +198,5 @@ if __name__ == "__main__":
     args = parse_args()
     if args.fetch:
         fetch_main()
-    plot_pct()
+    plt.style.use("tableau-colorblind10")
-    plot_main()
+    plot()