pandas में rows और columns कैसे filter करें
Read this guide in: English · मराठी · हिंदी
Columns select करो, boolean mask बनाओ, rows filter करो, फिर sort। उदाहरण: Online shop rows — Mumbai 120 + Pune 70 = 190.
दोस्तों! पूरी tables कहानी छुपाती हैं। Filters दिखाती हैं। आज select, boolean filter और sort — shop sales। सिर्फ़ खड़े steps। समझा?
संक्षिप्त उत्तर
खड़ा filter path:
- Select:
df[["city", "sales"]] - Mask:
mask = df["channel"] == "Online" - Filter:
online = df[mask].copy() - Sort:
online.sort_values("sales", ascending=False) - Check: sum → sample पर 190.
- Conditions
&/|और parentheses से जोड़ो।
select → mask → filter → sort → verify sum
Language: English · मराठी · हिंदी
इस guide से पहले क्या चाहिए?
- पहले से loaded DataFrame।
- Course: Selecting columns and rows.
Before और after
Before — full shop table; all rows total 260.
After — Online filter + sort; total 190.
Before: सब rows total 260. After: सिर्फ़ Online 190.
Sample shop table
| city | channel | sales |
|---|---|---|
| Mumbai | Online | 120 |
| Mumbai | Store | 40 |
| Pune | Online | 70 |
| Nashik | Store | 30 |
All = 260. Online = 190.
Select + boolean + sort
mask = df["channel"] == "Online"
online = df[mask].copy()
online["sales"].sum() # 190
online.sort_values("sales", ascending=False)
Combine: & / | with parentheses; English and/or मत; edit हो तो .copy().
df[(df["channel"] == "Online") & (df["sales"] >= 70)]
df[df["city"].isin(["Mumbai", "Pune"])]
Instagram-like
likes >= 70 filter करके descending sort — managers winners पहले देखें।
Ravindra Bagale's Tip
Filter ज़ोर से बोलो: “सिर्फ़ Online, फिर sales sort।” बोल नहीं सकते तो पहले code मत लिखो। समझा?
Practice task
- Store sum 70.
- Online sum 190.
- Online descending — पहली city Mumbai।
- Mumbai या Pune rows।
Learn it properly
Related: groupby aggregation · Handle missing values
Samajla ka? Select, mask, filter, sort, 190 verify। अब आगे चलें।
Frequently asked questions
loc vs just brackets?
Brackets are fine for simple column picks and masks. loc is clearer for label-based row+column work — both appear in the course.
Why .copy()?
Avoid SettingWithCopy surprises when you edit a filtered slice later.
How do I get top N?
sort_values then head(N), or nlargest on a column.
Filter on numbers?
df[df["sales"] >= 70] works the same way as text filters.
Multiple cities?
df[df["city"].isin(["Mumbai", "Pune"])]
Course links?
Selecting, boolean filters, sorting lessons in chapter 6.