Data analysis: ask better questions
Start with a question, inspect your data, clean missing values, then summarize. Never silently treat missing data as zero. Python’s standard library can calculate useful summaries; pandas helps with larger tabular workflows.
हिन्दी में समझें
पहले सवाल चुनें, डेटा देखें, गायब मान सँभालें, फिर सार निकालें। गायब डेटा को चुपचाप शून्य न मानें। मानक लाइब्रेरी सार निकाल सकती है; pandas बड़ी तालिकाओं में उपयोगी है।
अभ्यास: अंतिम बिक्री 210 से 240 करें; साफ डेटा का औसत 180 हो।
Hinglish mein samjhein
Pehle question chuno, data dekho, missing values handle karo, phir summary. Missing ko chupke se zero mat samjho. Standard library se summaries aur pandas se larger tables handle hote hain.
Practice: Last sale210 se240 karo; cleaned average180 hona chahiye.
A small Python example
sales = [120, 180, None, 210]
clean_sales = [x for x in sales if x is not None]
print("Total:", sum(clean_sales))
print("Average:", sum(clean_sales) / len(clean_sales))The browser playground runs real Python with Pyodide. Use sample variables instead of interactive input(). Browser package support differs from desktop Python.
Try this example in the Python playground →Practise this idea
Change the last sale from 210 to 240; the cleaned average should be 180.
Check answer & explanation
sales = [120, 180, None, 240]
clean_sales = [x for x in sales if x is not None]
print("Total:", sum(clean_sales))
print("Average:", sum(clean_sales) / len(clean_sales))The comprehension removes None without treating it as zero. The remaining sales total 540 across three entries, giving an average of 180.
Stuck in the course? Choose Check answer for either the challenge or quiz. Read the explanation and continue to the next lesson at your own pace.
Check your understanding
What should happen before analysis?
Check the quiz answer
Inspect and clean data
Reliable summaries depend on understanding the input data.
References & corrections
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