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There are many inbuilt functions and methods in python. Here are a few of the crucial functions:
1. Replace( ): The replace( ) method creates a replica of the string by replacing every instance of one substring with a different substring. It returns a new string where all the old substrings are replaced with a new substring.
Syntax: string.replace(old, new, count)
Old: old substring that we want to replace
New: old substring will be replaced with this new substring
Count: The number of times we want to replace the old with a new substring
Examples and uses:
- ## Replace all ‘two’ with ‘four’
Sample=”one, two, three, one, two, three, one, two, three”
Sample.replace(‘two’, ‘four’)
- ## Replace only first ‘two’ with ‘four’
Sample=”one, two, three, one, two, three, one, two, three”
Sample.replace(‘two’, ‘four’, 1)
- Replace function is very helpful in handling missing values in a data frame.
import numpy as np
import pandas as pd
# will replace Nan value in dataframe with value 99
data.replace(to_replace = np.nan, value = 99)
#Replace the null values with the mean:
df[‘A’].replace([numpy.nan], df[A].mean(), inplace=True)
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2. Map: The map function applies the given function to each item of a given iterable (list, tuple, etc.)
Syntax: map(function, iterables)
Function: a function that performs some action on each element of an iterable
Iterables: an iterable like sets, lists, tuples, etc
Examples and uses:
- ## Program to add number with itself
def addition(n):
return n + n
numbers = (1, 2, 3, 4)
result = map(addition, numbers)
list(result)
- ## To add two different numbers (we can pass multiple iterables also)
def addition(n1,n2):
return n1 + n2
n1 = (1, 2, 3, 4)
n2=(3,4,6,8)
result = map(addition, n1,n2)
list(result)
- ## Map with lambda function
num = (1, 2, 3, 4)
result = map(lambda x: x + x, num)
list(result)
- The map function can be used to change categorical values to numerical values in the data frame.
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3. Format( ): The format function is used to format any string.
Syntax: string.format(value1, value2…)
The format() method formats the specified value(s) and insert them inside the string’s placeholder. The placeholders can be identified using named indexes {name}, numbered indexes {1}, or even empty placeholders {}.
Examples and uses:
str1 = “My name is {fname}, I’m {age}”.format(fname = “Jack”, age = 20)
str2 = “My name is {0}, I’m {1}”.format(“Jack”,20)
str3 = “My name is {}, I’m {}”.format(“Jack”,20)
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4. Apply ( ): Apply function can be treated as an alternative to loops in data frames and series. It applies a function on each element in a pandas Series and each row or column in a pandas DataFrame.
Syntax: s.apply(function, convert_dtype=True, args=())
Function: Function to apply to each column or row.
s: series/Dataframe
Examples and uses:
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5. Filter: This function verifies whether each element in the series is true or false,
Syntax: filter(function, sequence)
function: function that tests if each element of a sequence is true or not.
sequence: sequence like sets, lists, tuples, or containers of any iterators.
Examples and uses:
- ## Program to return the ages that are greater than 18
ages = [6, 13, 16, 19, 25, 33]
def age(x):
if x < 18:
return False
else:
return True
adults = filter(age, ages)
for x in adults:
print(x)
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6. Range: It is used to return a sequence of numbers.
Syntax: range(start, stop, step)
Start: The integer number specifying with which number to start. By default, it is 0.
Stop: The integer number specifying the last number.
Step: An integer number specifying the incrementation. By default, it is 1.
Examples and uses:
- ## printing numbers from 3 to 20 with increments of 2
for i in range(3,20,2):
print(i, end=” “)
output: 3 5 7 9 11 13 15 17 19
- ## performing sum of natural number
sum = 0
for i in range(1, 11):
sum = sum + i
print(“Sum of first 10 natural number :”, sum)
Output: Sum of first 10 natural numbers: 55
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7. Isinstance( ): This function returns True if the object is specified types, and it will not match then return False.
Syntax: isinstance(obj, class)
Examples and uses:
numbers = [1, 2, 3]
result = isinstance(numbers, list)
print(numbers,’instance of list?’, result)
Output: [1, 2, 3] instance of list? True
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8. Any and All
Any Returns True if any of the items is True and returns False if empty or all are false. It can be thought of as – a sequence of OR operations.
Syntax: any(iterable)
Examples and uses:
- print (all([True, True, True, True]))
print (all([False, True, True, False]))
output: True
False
- ## It can be used to replace loops
for element in some_iterable:
if element:
return True
return False
The above code can be written as:
print(any([2 == 2, 3 == 2]))
print(any([True, False, False]))
print(any([False, False]))
output: True
True
False
All: It can be thought of as – a sequence of And operations. It returns True if all of the items are True.
Syntax: all(iterable)
Examples and uses:
- print (all([True, True, True, True]))
print (all([False, True, True, False]))
Output: True
False
- ## Can be used in place of loops
for element in iterable:
if not element:
return False
return True
The above code can be written as:
print(all([2 == 2, 3 == 2]))
print(all([2 > 1, 3 != 4]))
print(all([True, False, False]))
print(all([False, False]))
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9. Zip ( ): The zip() function in Python creates a single iterator object that contains the mapped values from different containers. It is used to map the index of several containers so that a single entity can access all of them.
Syntax: zip(iterator1, iterator2, iterator3 …)
Examples and uses:
- a = (“John”, “Charles”, “Mike”)
b = (“Jenny”, “Christy”, “Monica”, “Vicky”)
x = zip(a, b)
print(tuple(x))
output: ((‘John’, ‘Jenny’), (‘Charles’, ‘Christy’), (‘Mike’, ‘Monica’))
10. Append: The Python List append() method is used for adding elements to the end of the List.
Syntax: list.append(item)
Examples and uses:
a = [“apple”, “banana”, “cherry”]
b = [“Ford”, “BMW”, “Volvo”]
a.append(b)
output: [‘apple’, ‘banana’, ‘cherry’, [‘Ford’, ‘BMW’, ‘Volvo’]]
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