Filter by Condition - Pandas Step by Step

Build a boolean mask by testing each record's score field in a loop, then keep only the rows where the mask is True. With pandas, a single comparison on a column produces a boolean Series, and indexing a DataFrame with it returns the matching rows in one step.

By hand

Loop over records to build mask (one bool per row). Loop over indices to collect names where mask[i] is True.

                            naive.py
                        
Replay: real traced execution (multi-file project)

                            # trace: ignore records
records = [
    {'name': 'al', 'score': 55},
    {'name': 'bo', 'score': 82},
    {'name': 'cy', 'score': 71},
    {'name': 'di', 'score': 90},
]
k = 70
mask = []
for r in records:
    mask.append(r['score'] >= k)
names = []
for i in range(len(records)):
    if mask[i]:
        names.append(records[i]['name'])
print('RESULT:', names)

                        
1{'name': 'al', 'score': 55},⋯2records = [3    {'name': 'al', 'score': 55},4    {'name': 'bo', 'score': 82},⋯
2{'name': 'bo', 'score': 82},⋯3{'name': 'al', 'score': 55},4{'name': 'bo', 'score': 82},5{'name': 'cy', 'score': 71},⋯
3{'name': 'cy', 'score': 71},⋯4{'name': 'bo', 'score': 82},5{'name': 'cy', 'score': 71},6{'name': 'di', 'score': 90},⋯
4{'name': 'di', 'score': 90},⋯5    {'name': 'cy', 'score': 71},6    {'name': 'di', 'score': 90},7]⋯
5records = [1# trace: ignore records2records = [3    {'name': 'al', 'score': 55},⋯
6k ← 70⋯7]8k = 709mask = []⋯values this step70k
7mask ← []⋯8k = 709mask = []10for r in records:⋯values this step[]mask
8r ← {'name': 'al', 'score': 55}⋯9mask = []10for r in records:11    mask.append(r['score'] >= k)⋯values this step{'name': 'al', 'score': 55}r
9mask ← [False]⋯10for r in records:11    mask.append(r['score'] >= k)12names = []⋯values this step[] → [False]mask
10r ← {'name': 'bo', 'score': 82}⋯9mask = []10for r in records:11    mask.append(r['score'] >= k)⋯values this step{'name': 'al', 'score': 55} → {'name': 'bo', 'score': 82}r
11mask ← [False, True]⋯10for r in records:11    mask.append(r['score'] >= k)12names = []⋯values this step[False] → [False, True]mask
12r ← {'name': 'cy', 'score': 71}⋯9mask = []10for r in records:11    mask.append(r['score'] >= k)⋯values this step{'name': 'bo', 'score': 82} → {'name': 'cy', 'score': 71}r
13mask ← [False, True, True]⋯10for r in records:11    mask.append(r['score'] >= k)12names = []⋯values this step[False, True] → [False, True, True]mask
14r ← {'name': 'di', 'score': 90}⋯9mask = []10for r in records:11    mask.append(r['score'] >= k)⋯values this step{'name': 'cy', 'score': 71} → {'name': 'di', 'score': 90}r
15mask ← [False, True, True, True]⋯10for r in records:11    mask.append(r['score'] >= k)12names = []⋯values this step[False, True, True] → [False, True, True, True]mask
16for r in records:⋯9mask = []10for r in records:11    mask.append(r['score'] >= k)⋯
17names ← []⋯11    mask.append(r['score'] >= k)12names = []13for i in range(len(records)):⋯values this step[]names
18i ← 0⋯12names = []13for i in range(len(records)):14    if mask[i]:⋯values this step0i
19if mask[i]:⋯13for i in range(len(records)):14    if mask[i]:15        names.append(records[i]['name'])⋯
20i ← 1⋯12names = []13for i in range(len(records)):14    if mask[i]:⋯values this step0 → 1i
21if mask[i]:⋯13for i in range(len(records)):14    if mask[i]:15        names.append(records[i]['name'])⋯
22names ← ['bo']⋯14    if mask[i]:15        names.append(records[i]['name'])16print('RESULT:', names)values this step[] → ['bo']names
23i ← 2⋯12names = []13for i in range(len(records)):14    if mask[i]:⋯values this step1 → 2i
24if mask[i]:⋯13for i in range(len(records)):14    if mask[i]:15        names.append(records[i]['name'])⋯
25names ← ['bo', 'cy']⋯14    if mask[i]:15        names.append(records[i]['name'])16print('RESULT:', names)values this step['bo'] → ['bo', 'cy']names
26i ← 3⋯12names = []13for i in range(len(records)):14    if mask[i]:⋯values this step2 → 3i
27if mask[i]:⋯13for i in range(len(records)):14    if mask[i]:15        names.append(records[i]['name'])⋯
28names ← ['bo', 'cy', 'di']⋯14    if mask[i]:15        names.append(records[i]['name'])16print('RESULT:', names)values this step['bo', 'cy'] → ['bo', 'cy', 'di']names
29for i in range(len(records)):⋯12names = []13for i in range(len(records)):14    if mask[i]:⋯
30stdout ← RESULT: ['bo', 'cy', 'di']⋯15        names.append(records[i]['name'])16print('RESULT:', names)values this stepRESULT: ['bo', 'cy', 'di']stdout

With pandas

df['score'] >= k produces a boolean Series. df[mask] keeps only the rows where the Series is True and returns a sub-DataFrame.

                            library.py
                        

                            import pandas as pd
from dalib.display import set_display
set_display()

records = [
    {'name': 'al', 'score': 55},
    {'name': 'bo', 'score': 82},
    {'name': 'cy', 'score': 71},
    {'name': 'di', 'score': 90},
]
k = 70
df = pd.DataFrame(records)
mask = df['score'] >= k
filtered = df[mask]
print('mask:', mask.tolist())
print('names:', filtered['name'].tolist())
print('shape:', filtered.shape)
print('RESULT:', filtered['name'].tolist())

                        

mask: [False, True, True, True]
names: ['bo', 'cy', 'di']
shape: (3, 2)
RESULT: ['bo', 'cy', 'di']

Implementation notes

A boolean Series used as a row index is called boolean indexing. The Series must have the same index as the DataFrame; pandas aligns on labels before filtering.
The filtered result preserves the original integer index of matching rows (bo=1, cy=2, di=3 here). Use .reset_index(drop=True) afterward if you need a fresh 0-based index.
df['score'] >= k is vectorized — no Python loop over values. Pandas applies the comparison to the underlying numpy array directly (see mask-from-threshold in python-numpy ch06).
Cross-reference: filter-with-mask (python-numpy ch06) for the array version; filter-by-threshold (python-data-basics) for the pure-Python version.