12 Must‑Know NumPy & Pandas Functions to Supercharge Your Data Analysis

6 Efficient NumPy Functions

NumPy is a Python extension for scientific computing that provides powerful N‑dimensional array objects, a wide range of mathematical functions, and tools for integrating C/C++ and Fortran code, as well as linear algebra, Fourier transform, and random number generation capabilities.

1. argpartition()

Finds the indices of the N largest values in an array and can be used to sort those values.

x = np.array([12, 10, 12, 0, 6, 8, 9, 1, 16, 4, 6, 0])
index_val = np.argpartition(x, -4)[-4:]
index_val
# array([1, 8, 2, 0], dtype=int64)
np.sort(x[index_val])
# array([10, 12, 12, 16])

2. allclose()

Compares two arrays element‑wise within a tolerance and returns a boolean indicating whether they are close.

array1 = np.array([0.12, 0.17, 0.24, 0.29])
array2 = np.array([0.13, 0.19, 0.26, 0.31])
np.allclose(array1, array2, 0.1)  # False
np.allclose(array1, array2, 0.2)  # True

3. clip()

Limits the values in an array to a specified interval, clipping values outside the bounds.

x = np.array([3, 17, 14, 23, 2, 2, 6, 8, 1, 2, 16, 0])
np.clip(x, 2, 5)
# array([3, 5, 5, 5, 2, 2, 5, 5, 2, 2, 5, 2])

4. extract()

Extracts elements from an array that satisfy a given condition, supporting logical operations.

# Random integers
array = np.random.randint(20, size=12)
# Condition: odd numbers
cond = np.mod(array, 2) == 1
np.extract(cond, array)
# Example output: array([ 1, 19, 11, 13,  3])
# Direct condition example
np.extract(((array < 3) | (array > 15)), array)
# Example output: array([0, 1, 19, 16, 18, 2])

5. where()

Returns elements (or their indices) that satisfy a condition, similar to SQL's WHERE clause.

y = np.array([1,5,6,8,1,7,3,6,9])
np.where(y > 5)
# array([2, 3, 5, 7, 8], dtype=int64)
np.where(y > 5, "Hit", "Miss")
# array(['Miss', 'Miss', 'Hit', 'Hit', 'Miss', 'Hit', 'Miss', 'Hit', 'Hit'], dtype='<U4')

6. percentile()

Computes the n‑th percentile of array elements along a specified axis.

a = np.array([1,5,6,8,1,7,3,6,9])
print("50th Percentile of a, axis = 0 :", np.percentile(a, 50, axis=0))
# 50th Percentile of a, axis = 0 : 6.0
b = np.array([[10, 7, 4], [3, 2, 1]])
print("30th Percentile of b, axis = 0 :", np.percentile(b, 30, axis=0))
# 30th Percentile of b, axis = 0 : [5.1 3.5 1.9]

6 Efficient Pandas Functions

Pandas is a Python library that offers fast, flexible, and expressive data structures for handling structured (tabular, multidimensional, heterogeneous) and time‑series data.

1. read_csv(nrows=n)

Read only a subset of rows from a large CSV file to save memory and time.

import io, requests
url = "https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/datasets/AirPassengers.csv"
s = requests.get(url).content
df = pd.read_csv(io.StringIO(s.decode('utf-8')), nrows=10, index_col=0)

2. map()

Maps each value in a Series to a new value using a function, dictionary, or another Series.

dframe = pd.DataFrame(np.random.randn(4, 3), columns=list('ABC'), index=['India', 'USA', 'China', 'Russia'])
changefn = lambda x: "%.2f" % x
dframe['A'].map(changefn)

3. apply()

Applies a user‑defined function along an axis of the DataFrame.

fn = lambda x: x.max() - x.min()
dframe.apply(fn)

4. isin()

Filters rows based on whether column values are present in a list of values.

filter1 = df["value"].isin([112])
filter2 = df["time"].isin([1949.0])
df[filter1 & filter2]

5. copy()

Creates an explicit copy of a Pandas object to avoid unintended modifications.

data = pd.Series(["India", "Pakistan", "China", "Mongolia"])
new = data.copy()
new[1] = "Changed value"
print(new)
print(data)

6. select_dtypes()

Returns a subset of DataFrame columns based on their data types.

framex = df.select_dtypes(include="float64")

Additionally, pivot_table() provides Excel‑like pivot functionality for summarizing data.

school = pd.DataFrame({
    'A': ['Jay', 'Usher', 'Nicky', 'Romero', 'Will'],
    'B': ['Masters', 'Graduate', 'Graduate', 'Masters', 'Graduate'],
    'C': [26, 22, 20, 23, 24]
})
table = pd.pivot_table(school, values='A', index=['B', 'C'], columns=['B'], aggfunc=np.sum, fill_value="Not Available")
print(table)