Analyse und KPI-Berechnung
Berechnen Sie die monatliche Kohortenbindung, den Umsatz auf Produktebene und die gleitende Zahl aktiver Nutzer der letzten 30 Tage mit groupby, pivot und rolling.
Analyse und KPI-Berechnung ist eine kostenlose Pandas & NumPy Academy-Lektion auf CoddyKit. Dies ist Lektion 3 von 4. Du kannst die komplette Lektion unten kostenlos lesen – dann übst du sie direkt im Browser mit einem integrierten Code-Editor und einem KI-Tutor rund um die Uhr. Sie ist Teil des Pandas & NumPy Academy-Lernpfads, und dein Fortschritt wird über Web und CoddyKit-App synchronisiert. Der Pandas & NumPy Academy-Kurs umfasst insgesamt 4 Lektionen.
Teile dieser Lektion wurden noch nicht übersetzt und werden auf Englisch angezeigt.
Loading Analysis-Ready Data
With cleaning and feature engineering complete, load the analysis_ready.parquet checkpoint and begin computing KPIs. This stage is pure analysis — no more data cleaning. Having a clean, validated checkpoint lets you iterate quickly on KPI definitions without re-running the time-consuming ingestion and cleaning stages. The three KPIs to compute: monthly cohort retention, product-level revenue by category, and rolling 30-day active users.
import pandas as pd
df = pd.read_parquet('output/analysis_ready.parquet')
print(f'Loaded: {df.shape}')
print('KPIs to compute:')
print(' 1. Monthly cohort retention matrix')
print(' 2. Category revenue and margin ranking')
print(' 3. Rolling 30-day active users per region')KPI 1: Monthly Revenue by Category
The first KPI is monthly revenue by category. Group by both year_month and category, sum revenue, and pivot to get categories as columns and months as rows. This pivot table is the basis of the trend line chart and the top-category bar chart in the report. Apply a 3-month rolling mean to smooth the trend and highlight seasonality.
import pandas as pd
df = pd.read_parquet('output/analysis_ready.parquet')
# Monthly revenue by category
monthly_cat = (
df.groupby(['year_month', 'category'])['revenue']
.sum()
.reset_index()
.pivot(index='year_month', columns='category', values='revenue')
.fillna(0)
.sort_index()
)
# 3-month rolling average
monthly_cat_smooth = monthly_cat.rolling(3, min_periods=1).mean()
print('Monthly revenue by category (head):')
print(monthly_cat.round(0).head())
print('Shape:', monthly_cat.shape)KPI 2: Top Regions by Total Revenue
Rank regions by total revenue across the entire period. Use groupby().agg() to compute multiple statistics simultaneously: total revenue, order count, unique customers, and average order value. Sort by total revenue descending and keep the top 10. Compute the revenue share of each region relative to the grand total — this reveals concentration risk (if 80% of revenue comes from one region, the business is geographically exposed).
import pandas as pd
df = pd.read_parquet('output/analysis_ready.parquet')
region_kpi = (
df.groupby('region')
.agg(
total_revenue=('revenue', 'sum'),
order_count=('order_id', 'nunique'),
unique_customers=('customer_id', 'nunique'),
avg_order_value=('revenue', 'mean')
)
.reset_index()
.sort_values('total_revenue', ascending=False)
)
total = region_kpi['total_revenue'].sum()
region_kpi['revenue_share'] = (region_kpi['total_revenue'] / total).round(3)
print(region_kpi.head(10).to_string(index=False))KPI 3: Cohort Retention Matrix
The cohort retention matrix shows what percentage of customers from each acquisition cohort made at least one purchase in each subsequent period. Compute it in three steps: (1) assign each order a 'period number' = number of months since the customer's cohort month; (2) groupby cohort and period number, count distinct customers; (3) divide by the cohort size (period 0 count) to get retention rates. Pivot to cohort × period format.
import pandas as pd
df = pd.read_parquet('output/analysis_ready.parquet')
# Compute months since cohort start
df['cohort_period'] = (
(df['order_date'].dt.to_period('M') -
pd.PeriodIndex(df['cohort_month'], freq='M'))
.apply(lambda x: x.n)
)
cohort_data = (
df.groupby(['cohort_month', 'cohort_period'])['customer_id']
.nunique()
.reset_index()
.rename(columns={'customer_id': 'customers'})
)
# Pivot: rows=cohort, columns=period
cohort_pivot = cohort_data.pivot(
index='cohort_month', columns='cohort_period', values='customers'
)
cohort_sizes = cohort_pivot[0]
retention = cohort_pivot.divide(cohort_sizes, axis=0).round(3)
print('Retention matrix (first 3 cohorts):')
print(retention.head(3))KPI 4: Rolling 30-Day Active Users
Daily Active Users (DAU) and rolling window variants are core product KPIs. Compute the number of distinct customers who purchased in any given 30-day window. Start by computing daily unique purchasers, set the date as the index, then apply rolling('30D').apply(lambda x: x.nunique()) — but note that rolling on a DatetimeIndex requires specific aggregation. An alternative is to use a set-based rolling unique count via a custom window function.
import pandas as pd
df = pd.read_parquet('output/analysis_ready.parquet')
# Daily unique purchasers
daily = (
df.groupby('order_date')['customer_id']
.nunique()
.reset_index()
.rename(columns={'customer_id': 'daily_unique_customers'})
.set_index('order_date')
.sort_index()
)
# 30-day rolling sum (approximation: sum of daily unique purchasers)
# True rolling unique requires custom logic
daily['rolling_30d'] = daily['daily_unique_customers'].rolling('30D').sum()
print('Rolling 30-day active customers:')
print(daily.tail())Product-Level Revenue Ranking
For product-level analysis, rank all product IDs by total revenue and compute their cumulative revenue share. This reveals whether revenue follows a Pareto distribution (a common finding: ~20% of products generate ~80% of revenue). Compute the cumulative revenue column with .cumsum() and find the cut-off product count where cumulative share exceeds 80%. This analysis guides decisions about product portfolio prioritisation.
import pandas as pd
df = pd.read_parquet('output/analysis_ready.parquet')
product_rev = (
df.groupby('product_id')['revenue']
.sum()
.sort_values(ascending=False)
.reset_index()
)
total_rev = product_rev['revenue'].sum()
product_rev['rev_share'] = product_rev['revenue'] / total_rev
product_rev['cumulative_share'] = product_rev['rev_share'].cumsum()
pct80_cutoff = (product_rev['cumulative_share'] <= 0.80).sum()
total_products = len(product_rev)
print(f'Top {pct80_cutoff} of {total_products} products ({pct80_cutoff/total_products:.0%}) '
f'generate 80% of revenue (Pareto)')
print(product_rev.head())Saving All KPI Tables
Save each KPI table to a named Parquet file in the output directory. Naming convention: kpi_{name}.parquet. This makes the visualisation stage straightforward — it reads exactly the pre-computed table it needs without recomputing anything. Also save a combined KPI summary to a CSV for easy sharing with non-technical stakeholders who may open it in Excel.
import pandas as pd
# Save all KPI tables
monthly_cat.to_parquet('output/kpi_monthly_category_revenue.parquet')
region_kpi.to_parquet('output/kpi_region_summary.parquet', index=False)
retention.to_parquet('output/kpi_cohort_retention.parquet')
daily.to_parquet('output/kpi_rolling_active_users.parquet')
product_rev.to_parquet('output/kpi_product_ranking.parquet', index=False)
# Also a CSV summary for sharing
region_kpi.to_csv('output/region_summary.csv', index=False)
print('All KPI tables saved:')
print(' kpi_monthly_category_revenue.parquet')
print(' kpi_region_summary.parquet')
print(' kpi_cohort_retention.parquet')
print(' kpi_rolling_active_users.parquet')
print(' kpi_product_ranking.parquet')Statistical Validation of KPIs
Before finalising KPIs, run statistical checks: test whether monthly revenue growth is statistically significant using a one-sample t-test against zero growth, and check whether region revenue differences are significant using one-way ANOVA. Reporting 'top region by revenue' means more when you can confirm that the difference is unlikely due to random variation. Append p-values to the KPI tables as metadata columns.
import pandas as pd
import numpy as np
from scipy import stats
df = pd.read_parquet('output/analysis_ready.parquet')
# Is revenue growth statistically significant?
monthly_total = df.groupby('year_month')['revenue'].sum().sort_index()
month_changes = monthly_total.diff().dropna()
stat, p = stats.ttest_1samp(month_changes, popmean=0)
print(f'Monthly growth test: mean={month_changes.mean():.0f}, p={p:.4f}')
print('Significant positive trend?', month_changes.mean() > 0 and p < 0.05)
# ANOVA: do regions differ significantly?
groups = [g['revenue'].values for _, g in df.groupby('region')]
f, p_anova = stats.f_oneway(*groups)
print(f'Region ANOVA: F={f:.3f}, p={p_anova:.4f}')Computing Month-over-Month Growth Rate
The month-over-month (MoM) growth rate is a fundamental business KPI: growth = (current - previous) / previous × 100. Use pct_change() for clean computation. A 3-month moving average of MoM growth reveals the underlying trend, smoothing out monthly volatility. Negative growth rates are just as important to highlight as positive ones — they signal periods requiring investigation.
import pandas as pd
df = pd.read_parquet('output/analysis_ready.parquet')
monthly_total = (
df.groupby('year_month')['revenue'].sum().sort_index()
)
growth = monthly_total.pct_change() * 100
trend = growth.rolling(3, min_periods=1).mean()
kpi_growth = pd.DataFrame({
'revenue': monthly_total,
'mom_growth_pct': growth.round(2),
'trend_3m_avg': trend.round(2)
})
print(kpi_growth.tail(6))
print(f'\nBest month: {monthly_total.idxmax()} (${monthly_total.max():,.0f})')
print(f'Worst month: {monthly_total.idxmin()} (${monthly_total.min():,.0f})')Automated Anomaly Detection
Flag anomalous months automatically: months where revenue is more than 2 standard deviations from the trailing 6-month mean. These are worth investigating — they may reflect genuine business events (a successful campaign, a data outage) or data quality issues (duplicate data loaded twice). Anomaly detection is a valuable addition to any automated pipeline dashboard and helps analysts focus their investigation on the most important time periods.
import pandas as pd
import numpy as np
df = pd.read_parquet('output/analysis_ready.parquet')
monthly = df.groupby('year_month')['revenue'].sum().sort_index()
rolling_mean = monthly.rolling(6, min_periods=3).mean()
rolling_std = monthly.rolling(6, min_periods=3).std()
anomalies = monthly[
(monthly > rolling_mean + 2 * rolling_std) |
(monthly < rolling_mean - 2 * rolling_std)
]
print('Anomalous months (>2 std from 6-month rolling mean):')
if len(anomalies) > 0:
print(anomalies.round(0))
else:
print('None detected')KPI Summary Table
Compile a single executive summary table combining the top-level values from all KPIs: total revenue, total orders, unique customers, average order value, best region, best category, and average 30-day cohort retention at month 3. This one-table summary is the first page of the report — it gives stakeholders an immediate headline view before they dive into charts and detailed tables.
import pandas as pd
df = pd.read_parquet('output/analysis_ready.parquet')
retention = pd.read_parquet('output/kpi_cohort_retention.parquet')
region_kpi = pd.read_parquet('output/kpi_region_summary.parquet')
summary = {
'Total Revenue': f'${df["revenue"].sum():,.0f}',
'Total Orders': f'{df["order_id"].nunique():,}',
'Unique Customers': f'{df["customer_id"].nunique():,}',
'Avg Order Value': f'${df["revenue"].mean():.2f}',
'Best Region': region_kpi.iloc[0]["region"],
'Best Category': df.groupby("category")["revenue"].sum().idxmax(),
'Avg Month-3 Retention': f'{retention.get(3, pd.Series([0])).mean():.1%}'
}
for k, v in summary.items():
print(f'{k:25s}: {v}')Quick Check
Test your understanding of Data Analysis concepts from this lesson.
Lesson Recap
In this lesson you learned: groupby + pivot creates the monthly category revenue matrix and cohort retention table, rolling() computes smoothed trends and 30-day active user approximations, and statistical validation (t-test for growth, ANOVA for region differences) adds rigor to the KPI story. Next up we visualise all results in a multi-panel figure and export the final report.
Häufig gestellte Fragen
Ist die Lektion „Analyse und KPI-Berechnung“ kostenlos?
Ja — der vollständige Text von „Analyse und KPI-Berechnung“ ist hier im Web kostenlos zu lesen. Um sie interaktiv zu üben (integrierter Code-Editor und 24/7 KI-Tutor) und den Rest des Pandas & NumPy Academy-Kurses freizuschalten, upgrade auf CoddyKit PRO. Der Pandas & NumPy Academy-Kurs umfasst insgesamt 4 Lektionen.
Was lerne ich in „Analyse und KPI-Berechnung“?
Berechnen Sie die monatliche Kohortenbindung, den Umsatz auf Produktebene und die gleitende Zahl aktiver Nutzer der letzten 30 Tage mit groupby, pivot und rolling. Du übst Pandas & NumPy Academy mit praktischem Code, den du direkt im Browser ausführst, und ein 24/7 KI-Tutor beantwortet deine Fragen während du die Lektion bearbeitest.
Brauche ich Erfahrung, um Pandas & NumPy Academy zu starten?
Keine Vorkenntnisse erforderlich. Pandas & NumPy Academy auf CoddyKit ist für Anfänger bis fortgeschrittene Lernende strukturiert, sodass du hier starten oder von Anfang an beginnen und in deinem eigenen Tempo voranschreiten kannst. Dies ist Lektion 3 von 4.
Wie lange dauert die Lektion „Analyse und KPI-Berechnung“?
Die meisten CoddyKit-Lektionen dauern etwa 5–10 Minuten. Jede ist kompakt und interaktiv, sodass du stetig Fortschritte machst und genau dort weitermachst, wo du aufgehört hast – im Web und in der App.
Kann ich in dieser Pandas & NumPy Academy-Lektion Code schreiben und ausführen?
Ja. Jede Pandas & NumPy Academy-Lektion enthält einen integrierten Code-Editor, sodass du echten Code direkt in deinem Browser schreibst und ausführst und sofort KI-Feedback erhältst — ohne lokale Einrichtung erforderlich.
Alle Lektionen in diesem Kurs
- Projekteinrichtung und Datenimport
- Datenbereinigung und Feature Engineering
- Analyse und KPI-Berechnung
- Abschließende Visualisierung und Berichtexport