uw-escience - 2i2c Data

Cloud usage for the uw-escience cluster over the last 12 months. Data comes from the cloud GitHub release.

import pandas as pd
import plotly.express as px

# Read in the data for this cluster (these were generated by generate_pages.py)
CLUSTER = "uw-escience"
df_hub = pd.read_csv("uw-escience-by-hub.csv", parse_dates=["date"])
df_unique = pd.read_csv("uw-escience-unique.csv", parse_dates=["date"])
df_month = pd.read_csv("uw-escience-month-end.csv", parse_dates=["date"])

# These are for the little number boxes at the top
current_mau = int(df_unique["unique_users"].iloc[-1]) if not df_unique.empty else 0
peak_mau = int(df_unique["unique_users"].max()) if not df_unique.empty else 0

At a glance¶

Current MAU	Peak MAU (12mo)
0	0

Active users by hub¶

order = (
    df_hub.groupby("hub")["users"].mean()
    .sort_values(ascending=False).index.tolist()
)
fig = px.area(
    df_hub.sort_values("date"),
    x="date", y="users", color="hub",
    category_orders={"hub": order},
    title=f"Active users by hub on {CLUSTER}",
    labels={"users": "users"},
)
fig.show()

See How “total MAUs” are calculated.

Download: uw-escience-by-hub.csv

Unique active users¶

See How “unique MAUs” are calculated.

Download: uw-escience-unique.csv

Month-end unique MAUs¶

fig = px.bar(
    df_month,
    x=df_month["date"].dt.strftime("%Y-%m-%d"),
    y="unique_users",
    title=f"Month-end unique MAUs on {CLUSTER}",
    labels={"x": "month-end date", "unique_users": "unique users"},
)
fig.show()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[6], line 3
      1 fig = px.bar(
      2     df_month,
----> 3     x=df_month["date"].dt.strftime("%Y-%m-%d"),
      4     y="unique_users",
      5     title=f"Month-end unique MAUs on {CLUSTER}",
      6     labels={"x": "month-end date", "unique_users": "unique users"},

File ~/work/data/data/.nox/docs/lib/python3.13/site-packages/pandas/core/generic.py:6206, in NDFrame.__getattr__(self, name)
   6202             and name not in self._accessors
   6203             and self._info_axis._can_hold_identifiers_and_holds_name(name)
   6204         ):
   6205             return self[name]
-> 6206         return object.__getattribute__(self, name)

File ~/work/data/data/.nox/docs/lib/python3.13/site-packages/pandas/core/accessor.py:230, in Accessor.__get__(self, obj, cls)
    227 if obj is None:
    228     # we're accessing the attribute of the class, i.e., Dataset.geo
    229     return self._accessor
--> 230 return self._accessor(obj)

File ~/work/data/data/.nox/docs/lib/python3.13/site-packages/pandas/core/indexes/accessors.py:698, in CombinedDatetimelikeProperties.__new__(cls, data)
    695 elif isinstance(data.dtype, PeriodDtype):
    696     return PeriodProperties(data, orig)
--> 698 raise AttributeError("Can only use .dt accessor with datetimelike values")

AttributeError: Can only use .dt accessor with datetimelike values

See How monthly unique MAUs are calculated.

Download: uw-escience-month-end.csv

The full dataset (all clusters) is available in the cloud GitHub release.