from datetime import datetime
print(f"Date of last update: {datetime.now().strftime('%d.%m.%Y, %H:%M')}")
Date of last update: 28.07.2025, 03:50
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 5385.794872 std 4584.103142 min 714.000000 25% 2466.000000 50% 4074.000000 75% 6666.000000 max 32200.000000 Name: views, dtype: float64
Grundlagen der Mathematik¶
Histogram of distribution¶
Information about distribution¶
count 59.000000 mean 4250.440678 std 2591.133844 min 803.000000 25% 2524.500000 50% 3556.000000 75% 5472.000000 max 12225.000000 Name: views, dtype: float64
Lineare Algebra 1¶
Histogram of distribution¶
Information about distribution¶
count 49.000000 mean 4721.673469 std 2871.809965 min 1309.000000 25% 2555.000000 50% 4127.000000 75% 5766.000000 max 15155.000000 Name: views, dtype: float64
Maßtheorie¶
Histogram of distribution¶
Information about distribution¶
count 9.000000 mean 1845.333333 std 1183.275644 min 711.000000 25% 1095.000000 50% 1581.000000 75% 2131.000000 max 4649.000000 Name: views, dtype: float64
Real Analysis¶
Histogram of distribution¶
Information about distribution¶
count 89.000000 mean 1272.168539 std 1286.835824 min 434.000000 25% 722.000000 50% 1042.000000 75% 1310.000000 max 10371.000000 Name: views, dtype: float64
Linear algebra¶
Histogram of distribution¶
Information about distribution¶
count 42.000000 mean 869.952381 std 222.472794 min 590.000000 25% 705.250000 50% 834.000000 75% 945.000000 max 1603.000000 Name: views, dtype: float64
Measure theory¶
Histogram of distribution¶
Information about distribution¶
count 8.00000 mean 1023.75000 std 217.54983 min 678.00000 25% 888.25000 50% 1031.50000 75% 1187.00000 max 1304.00000 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.