from datetime import datetime
print(f"Date of last update: {datetime.now().strftime('%d.%m.%Y, %H:%M')}")
Date of last update: 12.01.2026, 03:47
Utility functions and imports¶
%load_ext jupyter_ai_magics
%load_ext autoreload
%autoreload 2
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import display, Markdown
from mfnf import MFNF
def md(text):
display(Markdown(text))
df = MFNF().aggregate_pageviews()
Page view distribution over complete book¶
from datetime import datetime, timedelta
def analyze_page_distribution(df):
one_year_ago = datetime.now() - timedelta(days=365)
df = df[(df["timestamp"] > one_year_ago) & (df["page_name"] is not None)]
df = df[["page_name", "views"]].groupby("page_name").sum()
md("### Histogram of distribution")
plt.figure(figsize=(10, 6))
sns.histplot(df["views"], kde=True, log_scale=True, bins="sqrt")
plt.title("Distribution of page views in the last year")
plt.xlabel("Number of page views")
plt.ylabel("Frequency")
plt.grid(True)
plt.show()
md("### Information about distribution")
display(df["views"].describe())
analyze_page_distribution(df.copy())
Page view distribution per book¶
from mfnf import books
for book in books:
md(f"### {book}")
analyze_page_distribution(df[df["book_name"] == book].copy())
Analysis 1¶
Histogram of distribution¶
Information about distribution¶
count 117.000000 mean 4379.923077 std 3616.014824 min 763.000000 25% 2015.000000 50% 3591.000000 75% 5562.000000 max 28078.000000 Name: views, dtype: float64
Grundlagen der Mathematik¶
Histogram of distribution¶
Information about distribution¶
count 59.000000 mean 3497.016949 std 1936.236623 min 955.000000 25% 2170.500000 50% 2844.000000 75% 4212.000000 max 9577.000000 Name: views, dtype: float64
Lineare Algebra 1¶
Histogram of distribution¶
Information about distribution¶
count 49.000000 mean 4202.959184 std 3200.743780 min 1168.000000 25% 1752.000000 50% 3652.000000 75% 4940.000000 max 16234.000000 Name: views, dtype: float64
Maßtheorie¶
Histogram of distribution¶
Information about distribution¶
count 9.000000 mean 1389.555556 std 691.357742 min 538.000000 25% 1090.000000 50% 1291.000000 75% 1419.000000 max 3029.000000 Name: views, dtype: float64
Real Analysis¶
Histogram of distribution¶
Information about distribution¶
count 89.000000 mean 1586.056180 std 1550.273842 min 307.000000 25% 994.000000 50% 1306.000000 75% 1631.000000 max 11157.000000 Name: views, dtype: float64
Linear algebra¶
Histogram of distribution¶
Information about distribution¶
count 42.000000 mean 1092.785714 std 263.454678 min 651.000000 25% 900.250000 50% 1112.500000 75% 1192.250000 max 1856.000000 Name: views, dtype: float64
Measure theory¶
Histogram of distribution¶
Information about distribution¶
count 8.000000 mean 1485.625000 std 347.536201 min 854.000000 25% 1339.750000 50% 1516.500000 75% 1716.250000 max 1908.000000 Name: views, dtype: float64
License of this report¶
Copyright 2025 Stephan Kulla ("Kulla")
Licensed under the Apache License, Version 2.0 (the "Apache License") and Creative Commons Attribution 4.0 International (the "CC-BY License"). You may choose either of these licenses to govern your use of this project.
You may obtain a copy of the Apache License at: http://www.apache.org/licenses/LICENSE-2.0
You may obtain a copy of the CC-BY License at: https://creativecommons.org/licenses/by/4.0/
Unless required by applicable law or agreed to in writing, software and content distributed under the Apache License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the Apache License for the specific language governing permissions and limitations under the License.
Under the CC-BY License, you are free to share and adapt the material provided you give appropriate credit, provide a link to the license, and indicate if changes were made. See the CC-BY License for full details.
Third-Party Components and Licenses: This product also includes third-party components which are distributed under their respective licenses. Please refer to the NOTICE file for details.