Filter with Multiple Conditions - Pandas Step by Step

Combine two per-row boolean tests with Python's and in a loop to build a single mask, then keep matching rows. With pandas, element-wise & combines two boolean Series before indexing the DataFrame — and and or are not valid for Series and will raise an error.

By hand

Loop over records, apply both conditions with and, and append the combined bool to mask. 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', 'age': 20, 'score': 55},
    {'name': 'bo', 'age': 25, 'score': 82},
    {'name': 'cy', 'age': 22, 'score': 71},
    {'name': 'di', 'age': 28, 'score': 90},
]
lo, hi = 21, 88
mask = []
for r in records:
    mask.append(r['age'] > lo and r['score'] < hi)
names = []
for i in range(len(records)):
    if mask[i]:
        names.append(records[i]['name'])
print('RESULT:', names)

                        
1{'name': 'al', 'age': 20, 'score': 55},⋯2records = [3    {'name': 'al', 'age': 20, 'score': 55},4    {'name': 'bo', 'age': 25, 'score': 82},⋯
2{'name': 'bo', 'age': 25, 'score': 82},⋯3{'name': 'al', 'age': 20, 'score': 55},4{'name': 'bo', 'age': 25, 'score': 82},5{'name': 'cy', 'age': 22, 'score': 71},⋯
3{'name': 'cy', 'age': 22, 'score': 71},⋯4{'name': 'bo', 'age': 25, 'score': 82},5{'name': 'cy', 'age': 22, 'score': 71},6{'name': 'di', 'age': 28, 'score': 90},⋯
4{'name': 'di', 'age': 28, 'score': 90},⋯5    {'name': 'cy', 'age': 22, 'score': 71},6    {'name': 'di', 'age': 28, 'score': 90},7]⋯
5records = [1# trace: ignore records2records = [3    {'name': 'al', 'age': 20, 'score': 55},⋯
6hi ← 88, lo ← 21⋯7]8lo, hi = 21, 889mask = []⋯values this step88hi21lo
7mask ← []⋯8lo, hi = 21, 889mask = []10for r in records:⋯values this step[]mask
8r ← {'name': 'al', 'age': 20, 'score': 55}⋯9mask = []10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)⋯values this step{'name': 'al', 'age': 20, 'score': 55}r
9mask ← [False]⋯10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)12names = []⋯values this step[] → [False]mask
10r ← {'name': 'bo', 'age': 25, 'score': 82}⋯9mask = []10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)⋯values this step{'name': 'al', 'age': 20, 'score': 55} → {'name': 'bo', 'age': 25, 'score': 82}r
11mask ← [False, True]⋯10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)12names = []⋯values this step[False] → [False, True]mask
12r ← {'name': 'cy', 'age': 22, 'score': 71}⋯9mask = []10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)⋯values this step{'name': 'bo', 'age': 25, 'score': 82} → {'name': 'cy', 'age': 22, 'score': 71}r
13mask ← [False, True, True]⋯10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)12names = []⋯values this step[False, True] → [False, True, True]mask
14r ← {'name': 'di', 'age': 28, 'score': 90}⋯9mask = []10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)⋯values this step{'name': 'cy', 'age': 22, 'score': 71} → {'name': 'di', 'age': 28, 'score': 90}r
15mask ← [False, True, True, False]⋯10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)12names = []⋯values this step[False, True, True] → [False, True, True, False]mask
16for r in records:⋯9mask = []10for r in records:11    mask.append(r['age'] > lo and r['score'] < hi)⋯
17names ← []⋯11    mask.append(r['age'] > lo and r['score'] < hi)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'])⋯
28for i in range(len(records)):⋯12names = []13for i in range(len(records)):14    if mask[i]:⋯
29stdout ← RESULT: ['bo', 'cy']⋯15        names.append(records[i]['name'])16print('RESULT:', names)values this stepRESULT: ['bo', 'cy']stdout

With pandas

(df['age'] > lo) & (df['score'] < hi) combines two boolean Series elementwise. Parentheses around each condition are required because & has higher operator precedence than > and <.

                            library.py
                        

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

records = [
    {'name': 'al', 'age': 20, 'score': 55},
    {'name': 'bo', 'age': 25, 'score': 82},
    {'name': 'cy', 'age': 22, 'score': 71},
    {'name': 'di', 'age': 28, 'score': 90},
]
lo, hi = 21, 88
df = pd.DataFrame(records)
mask = (df['age'] > lo) & (df['score'] < hi)
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, False]
names: ['bo', 'cy']
shape: (2, 3)
RESULT: ['bo', 'cy']

Implementation notes

Use & (bitwise AND) and | (bitwise OR) to combine boolean Series — NOT Python's and / or. and/or call bool() on the whole Series, which raises ValueError: The truth value of a Series is ambiguous.
Parentheses around each condition are mandatory: (df['a'] > lo) & (df['b'] < hi). Without them, & binds tighter than > and the expression parses incorrectly.
This is the same operator rule as NumPy: (a > lo) & (a < hi) — see combine-two-masks (python-numpy ch06).
For more than two conditions, chain & or |: (c1) & (c2) & (c3). Alternatively, df.query("age > 21 and score < 88") allows Python-style and/or syntax inside a string.