Python Archives - Page 14 of 165

Check if all Elements in a List are None in Python

A collection is an ordered list of values. There could be various types of values. A list is a mutable container. This means that existing ones can be added to, deleted from, or changed.

The Python list represents the mathematical concept of a finite sequence. List values are referred to as list items or list elements. The same value may appear multiple times in a list. Each event is regarded as a distinct element.

Given a list, the task is to check if all elements in a list are none or not.

Examples:

Input:

givenlist = [None, None, None, None, None, None]

Output:

The givenlist's elements are all None

Check if all items in a list are none in python

There are several ways to check if all items in a list are none some of them are:

Using for loop
Writing custom function
Using List Comprehension
Using count() function

Method #1:Using for loop

Iterate through all of the items in a list using a for loop, checking if each item is None or not. Break the loop as soon as a non None object is identified, as this indicates that all items in the list are not None. If the loop ends without finding even a single non-None object, it proves that the list’s items are all None.

Let’s make a function that takes a list and checks whether any of the things are none or not using the logic described above.

Below is the implementation:

# function which checks if all the elements are none in the list are none
def checkNone(givenlist):
    # initializing result to true
    res = True
    # traversing the givenlist
    for element in givenlist:
        if element is not None:
          # all the elements are not null hence return false
            return False
    # if all the elements are null then return the result i.e true
    return res


# driver code
# given list
givenlist = [None, None, None, None, None, None]
# passing givenlist to checkNone function
answer = checkNone(givenlist)
if(answer):
    print("The givenlist's elements are all None")
else:
    print("givenlist elements are not null")

Output:

The givenlist's elements are all None

Method #2:Writing custom function

Create a generic function that checks whether all items in a list are the same and also matches the specified element.

As an argument, this function takes a list and an object. Then, using the same logic as the previous example, determines if all items in the given list match the given object. This feature can be used to see if any of the things in a list are None.

Below is the implementation:

# function which checks if all the elements are none in the list are none
def checkNone(givenlist, value):
    # initializing result to true
    res = True
    # traversing the givenlist
    for element in givenlist:
        if element != value:
          # all the elements are not null hence return false
            return False
    # if all the elements are null then return the result i.e true
    return res


# driver code
# given list
givenlist = [None, None, None, None, None, None]
# passing givenlist to checkNone function
answer = checkNone(givenlist, None)
if(answer):
    print("The givenlist's elements are all None")
else:
    print("givenlist elements are not null")

Output:

The givenlist's elements are all None

Method #3:Using List Comprehension

We generated a Boolean list from the current list using list comprehension. Each element in this bool list corresponds to an item in the main list. Basically, in List Comprehension, we iterated through the given list, checking if each member was None or not. If it’s None, put True in the bool list; otherwise, put False. All() was used to see whether the bool list contained only True or not. If the all() function returns True, our main list only contains None.

Below is the implementation:

# function which checks if all the elements are none in the list are none
def checkNone(givenlist, value):
    # all() +list comprehension  function
    return all([element == value for element in givenlist])


# driver code
# given list
givenlist = [None, None, None, None, None, None]
# passing givenlist to checkNone function
answer = checkNone(givenlist, None)
if(answer):
    print("The givenlist's elements are all None")
else:
    print("givenlist elements are not null")

Output:

The givenlist's elements are all None

Method #4:Using count() function

Using the count() function, we can count the number of none values in the list.

Return True if the count of none values equals the length of the list, otherwise False.

Below is the implementation:

# function which checks if all the elements are none in the list are none
def checkNone(givenlist):
    # using count function
    # counting none values in list
    countnone = givenlist.count(None)
    # return true if count is equal to length
    return countnone == len(givenlist)


# driver code
# given list
givenlist = [None, None, None, None, None, None]
# passing givenlist to checkNone function
answer = checkNone(givenlist)
if(answer):
    print("The givenlist's elements are all None")
else:
    print("givenlist elements are not null")

Output:

The givenlist's elements are all None

Related Programs:

Check if all Elements in a List are None in Python Read More »

Python: Pretty Print Nested Dictionaries – Dict of Dicts

Python / By Vikram Chiluka

Dictionaries are Python’s implementation of an associative list, which may be a arrangement . A dictionary may be a collection of key-value pairs that are stored together. A key and its value are represented by each key-value pair.

To an infinite depth, a dictionary may include another dictionary, which can contain dictionaries, and so on. This is referred to as a nested dictionary.

Nested dictionaries are one among several ways during which structured information is represented (similar to records or structures in other languages).

In this article, we’ll look at how to print a nested dictionary in a visually beautiful and readable format.

Examples:

Input:

nesteddict = {
'hello': {'www': 100, 'yyy': 'Helloworld'},
'this': {'www': 'vikram', 'age': 20, 'DOB': 'FEB'},
'BTechGeeks': {'PLACE': 'HYDERABAD', 'PINCODE': 500000,
'PYTHON': {'FUNCTIONS': 'Built in', 'year': 1999}},
}

Output:

{
    "hello": {
        "www": 100,
        "yyy": "Helloworld"
    },
    "this": {
        "www": "vikram",
        "age": 20,
        "DOB": "FEB"
    },
    "BTechGeeks": {
        "PLACE": "HYDERABAD",
        "PINCODE": 500000,
        "PYTHON": {
            "FUNCTIONS": "Built in",
            "year": 1999
        }
    }
}

Print nested dictionaries in pretty way

There are several ways to print nested dictionaries in pretty way some of them are:

Using generic function
Using json module
Using pandas to print as table

1)Using generic function

We can only call the values() function of a dictionary for a normal dictionary to get an iterable sequence of values. However, a value can be another dictionary object in a nested dictionary. To do this, we need to call the values() function again and get a new iterative sequence of values and search for dictation objects in those values. This is possible with recurrence in a simple way.

To print all of the key-values in this dictionary, we wrote a function that will recursively go through the nested dictionary and print all of the key-values.

Below is the implementation:

def printNested(nesteddict):
    # Traverse the dictionary
    for key, value in nesteddict.items():
        # If the value is of the dictionary type, then print
        # all of the key and values within the nested dictionary.
        if isinstance(value, dict):
            print(key, "  : {\n")
            printNested(value)
            print("\n}\n")

        else:
            print("\t", key, " : ", value)


# given nested_dictionary
nesteddict = {
    'hello':    {'www': 100, 'yyy': 'Helloworld'},
    'this':    {'www': 'vikram', 'age': 20, 'DOB': 'FEB'},
    'BTechGeeks':    {'PLACE': 'HYDERABAD', 'PINCODE': 500000,
                      'PYTHON': {'FUNCTIONS': 'Built in', 'year': 1999}},
}
# passing nested dictionary to printNested Function
printNested(nesteddict)

Output:

hello   : {

	 www  :  100
	 yyy  :  Helloworld

}

this   : {

	 www  :  vikram
	 age  :  20
	 DOB  :  FEB

}

BTechGeeks   : {

	 PLACE  :  HYDERABAD
	 PINCODE  :  500000
PYTHON   : {

	 FUNCTIONS  :  Built in
	 year  :  1999

}
}

2)Using json module

Rather than writing our own functions, we can use the json module to print a dictionary of dictionaries in a format that looks like json.

Below is the implementation:

import json as json
# given nested_dictionary
nesteddict = {
    'hello':    {'www': 100, 'yyy': 'Helloworld'},
    'this':    {'www': 'vikram', 'age': 20, 'DOB': 'FEB'},
    'BTechGeeks':    {'PLACE': 'HYDERABAD', 'PINCODE': 500000,
                      'PYTHON': {'FUNCTIONS': 'Built in', 'year': 1999}},
}
# using json to print the nested dict
print(json.dumps(nesteddict, indent=4))

Output:

{
    "hello": {
        "www": 100,
        "yyy": "Helloworld"
    },
    "this": {
        "www": "vikram",
        "age": 20,
        "DOB": "FEB"
    },
    "BTechGeeks": {
        "PLACE": "HYDERABAD",
        "PINCODE": 500000,
        "PYTHON": {
            "FUNCTIONS": "Built in",
            "year": 1999
        }
    }
}

3)Using pandas to print as table

We can print the nested dictionary a table using the pandas module.

Below is the implementation:

import pandas as pd
# given nested_dictionary
nesteddict = {
    'hello':    {'www': 100, 'yyy': 'Helloworld'},
    'this':    {'www': 'vikram', 'age': 20, 'DOB': 'FEB'},
    'BTechGeeks':    {'PLACE': 'HYDERABAD', 'PINCODE': 500000,
                      'PYTHON': {'FUNCTIONS': 'Built in', 'year': 1999}},
}
print(pd.DataFrame(nesteddict).T)

Output:

                         DOB            PINCODE      PLACE        ...     age      www       yyy
hello                 NaN            NaN              NaN           ...     NaN    100         Helloworld
this                   FEB              NaN              NaN           ...     20        vikram    NaN
BTechGeeks      NaN            500000         HYDERABAD ... NaN      NaN        NaN

[3 rows x 7 columns]

Related Programs:

Python: Pretty Print Nested Dictionaries – Dict of Dicts Read More »

Python: Remove characters from string by regex and 4 other ways

Python / By Satyabrata Jena

Removing characters from string by regex and many other ways in Python.

In this article we will get to know about deleting characters from string in python by regex() and various other functions.

Removing characters from string by regex :

sub() function of regex module in Python helps to get a new string by replacing a particular pattern in the string by a string replacement. If no pattern found, then same string will be returned.

Removing all occurrences of a character from string using regex :

Let we want to delete all occurrence of ‘a’ from a string. We can implement this by passing in sub() where it will replace all ‘s’ with empty string.

#Program :

import re
origin_string = "India is my country"

pattern = r'a'

# If found, all 'a' will be replaced by empty string
modif_string = re.sub(pattern, '', origin_string )
print(modif_string)

Output :
Indi is my country

Removing multiple characters from string using regex in python :

Let we want to delete all occurrence of ‘a’, ‘i’. So we have to replace these characters by empty string.

#Program :

import re
origin_string = "India is my country"
pattern = r'[ai]'
# If found, all 'a' & 'i' will be replaced by empty string
modif_string = re.sub(pattern, '', origin_string )
print(modif_string)

Output :
Ind s my country

Removing characters in list from the string in python :

Let we want to delete all occurrence of ‘a’ & ‘i’ from a string and also these characters are in a list.

#Program :

import re
origin_string = "India is my country"
char_list = ['i','a']
pattern = '[' + ''.join(char_list) + ']'
# All charcaters are removed if matched by pattern
modif_string = re.sub(pattern, '', origin_string)
print(modif_string)

Output :
Ind s my country

Removing characters from string using translate() :

str class of python provide a function translate(table). The characters in the string will be replaced on the basis of mapping provided in translation table.

Removing all occurrence of a character from the string using translate() :

Let we want to delete all occurrence of ‘i’ in a string. For this we have to pass a translation table to translate() function where ‘i’ will be mapped to None.

#Program :

origin_string = "India is my country"
# If found, remove all occurence of 'i' from string
modif_string = origin_string.translate({ord('i'): None})
print(modif_string)

Output :
Inda s my country

Removing multiple characters from the string using translate() :

Let, we want to delete ‘i’, ‘y’ from the above string.

#Program :

org_string= "India is my country"
list_char = ['y', 'i']
# Remove all 's', 'a' & 'i' from the string
mod_string = org_string.translate( {ord(elem): None for elem in list_char} )
print(mod_string)

Output :
Inda s m countr

**Removing characters from string using replace() :**

Python provides str class, from which the replace() returns the copy of a string by replacing all occurrence of substring by a replacement. In the above string we will try to replace all ‘i’ with ‘a’.

#Program :

origin_string = "India is my country"
# Replacing all of 's' with 'a'
modif_string = origin_string.replace('i', 'a')
print(modif_string)

Output :
Indaa as my country

Removing characters from string using join() and generator expression :

Let we have a list with some characters. For removing all characters from string that are in the list, we would iterate over each characters in the string and join them except those which are not in the list.

#Program :

origin_string= "India is my country"
list_char = ['i', 'a', 's']
# Removes all occurence of 'i', 'a','s' from the string
modif_string = ''.join((elem for elem in origin_string if elem not in list_char))
print(modif_string)

Output :
Ind  my country

Removing characters from string using join and filter() :

filter() function filter the characters from string based on logic provided in call back function. If we provide a call back function as lambda function, it checks if the characters in the list are filtered are not. After that it joins the remaining characters to from a new string i.e. it eliminates all occurrence of characters that are in the list form a string.

#Programn :

origin_string = "India is my country"
list_char = ['i', 'a', 's']
# To remove all characters in list, from the string
modif_string = ''.join(filter(lambda k: k not in list_char, origin_string))
print(modif_string)

Output :
Ind  my country

Python: Remove characters from string by regex and 4 other ways Read More »

Pandas: Sort rows or columns in Dataframe based on values using Dataframe.sort_values()

Python / By Satyabrata Jena

How to sort rows or columns in Dataframe based on values using Dataframe.sort_values() in Python ?

In this article we will get to know about how to sort rows in ascending & descending order based on values in columns and also sorting columns based on value in rows using member function DataFrame.sort_values().

DataFrame.sort_values() :

Pandas library contains a Dataframe class which provide a function to arrange the contents of dataframe.

Syntax : DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')

where,

by: Column names or index labels where sorting is to be done.
axis: If axis=0. names by argument will be taken as column names and If axis=1, names by argument will be taken as row index labels
ascending: If True: Ascending Sorting , Else: Descending Sorting
inplace: When true in-place operation performed in dataframe
na_position: If True: It will sort the current dataframe , Else: Return a sorted copy of dataframe instead of modifying the dataframe.

Sorting Dataframe rows based on a single column :

To sort rows in dataframe based on ‘Team’ column, we will pass ‘Team’ as argument.

#Program :

import pandas as sc

# List of Tuples as Players
players = [ ('Curran', 325, 'CSK') ,
             ('Ishan', 481, 'MI' ) ,
             ('Vijay', 106, 'SRH') ,
             ('Pant', 224, 'Delhi' ) ,
             ('Seifert', 65, 'KKR' ) ,
             ('Hooda', 440, 'PK' )
              ]

# To Create DataFrame object
datafObjs = sc.DataFrame(players, columns=['Name', 'Runs', 'Team'], index=['i', 'ii', 'iii', 'iv', 'v', 'vi'])

# Sorting the rows of dataframe by column 'Team'
datafObjs = datafObjs.sort_values(by ='Team' )

print("Now the sorted Dataframe based on column 'Team' is : ")
print(datafObjs)

Output :
 Now the sorted Dataframe based on column 'Team' is :
        Name  Runs   Team
i     Curran   325    CSK
iv      Pant   224  Delhi
v    Seifert    65    KKR
ii     Ishan   481     MI
vi     Hooda   440     PK
iii    Vijay   106    SRH

Sorting Dataframe rows based on multiple column :

To sort all rows of dataframe based on Team & Runs column.

#Program :

import pandas as sc

# List of Tuples as Players
players = [ ('Curran', 325, 'CSK') ,
             ('Ishan', 481, 'MI' ) ,
             ('Vijay', 106, 'SRH') ,
             ('Pant', 224, 'Delhi' ) ,
             ('Seifert', 65, 'KKR' ) ,
             ('Hooda', 440, 'PK' )
              ]

# To Create DataFrame object
datafObjs = sc.DataFrame(players, columns=['Name', 'Runs', 'Team'], index=['i', 'ii', 'iii', 'iv', 'v', 'vi'])

# Sorting the rows of dataframe by column 'Team'
datafObjs = datafObjs.sort_values(by =['Runs', 'Team'])

print("Now the sorted Dataframe on the basis of columns 'Name' & 'Marks' : ")
print(datafObjs)

Output :
Now the sorted Dataframe on the basis of columns 'Name' & 'Marks' :
        Name   Runs   Team
v     Seifert    65       KKR
iii    Vijay      106     SRH
iv     Pant      224    Delhi
i      Curran   325     CSK
vi     Hooda   440     PK
ii     Ishan      481     MI

Sorting Dataframe rows based on columns in Descending Order :

Here we will sort the dataframe in descending order by passing value as false in argument ascending.

#Program :

import pandas as sc

# List of Tuples as Players
players = [ ('Curran', 325, 'CSK') ,
             ('Ishan', 481, 'MI' ) ,
             ('Vijay', 106, 'SRH') ,
             ('Pant', 224, 'Delhi' ) ,
             ('Seifert', 65, 'KKR' ) ,
             ('Hooda', 440, 'PK' )
              ]

# To Create DataFrame object
datafObjs = sc.DataFrame(players, columns=['Name', 'Runs', 'Team'], index=['i', 'ii', 'iii', 'iv', 'v', 'vi'])

# Sortimg rows of dataframe by column 'Name' in descending manner
datafObjs = datafObjs.sort_values(by ='Name' , ascending=False)

print("Now the sorted Dataframe on basis of column 'Name' in Descending manner : ")
print(datafObjs)

Output :
Now the sorted Dataframe on basis of column 'Name' in Descending manner :
        Name   Runs   Team
iii     Vijay      106    SRH
v     Seifert     65      KKR
iv    Pant       224     Delhi
ii     Ishan      481     MI
vi     Hooda   440     PK
i      Curran     325    CSK

Sorting Dataframe rows based on a column in Place :

Here we will sort the dataframe based on single column in place with argument inplace and value True.

#Program :

import pandas as sc

# List of Tuples as Players
players = [ ('Curran', 325, 'CSK') ,
             ('Ishan', 481, 'MI' ) ,
             ('Vijay', 106, 'SRH') ,
             ('Pant', 224, 'Delhi' ) ,
             ('Seifert', 65, 'KKR' ) ,
             ('Hooda', 440, 'PK' )
              ]

# To Create DataFrame object
datafObjs = sc.DataFrame(players, columns=['Name', 'Runs', 'Team'], index=['i', 'ii', 'iii', 'iv', 'v', 'vi'])

# Sorting e rows of the dataframe by column 'Name' inplace
datafObjs.sort_values(by='Team' , inplace=True)

print("Now the sorted Dataframe based on a single column 'Team' inplace : ")
print(datafObjs)

Output :
Now the sorted Dataframe based on a single column 'Team' inplace :
        Name  Runs   Team
i      Curran   325    CSK
iv     Pant     224     Delhi
v      Seifert    65    KKR
ii      Ishan    481     MI
vi     Hooda   440     PK
iii     Vijay     106     SRH

Sorting columns of a Dataframe based on a single or multiple rows :

Let’s take a example where column of a dataframe is to be sorted on the basis of single or multiple rows.

#program :

import pandas as sc

# List of Tuples as matrix
matrix = [(11, 2, 33),
          (4, 55, 6),
          (77, 8, 99),
          ]

# To create a DataFrame object like a Matrix
datafObjs = sc.DataFrame(matrix, index=list('abc'))

print("Sorting the daataframe based on single rows or multiple rows: ")
print(datafObjs)

Output :
Sorting the daataframe based on single rows or multiple rows:
    0   1   2
a  11   2  33
b   4  55   6
c  77   8  99

Sorting columns of a Dataframe based on a single row :

Now from above dataframe let’s sort the dataframe on basis of ‘b’ row.

#program :

import pandas as sc

# List of Tuples as matrix
matrix = [(11, 2, 33),
          (4, 55, 6),
          (77, 8, 99),
          ]

# To create a DataFrame object like a Matrix
datafObjs = sc.DataFrame(matrix, index=list('abc'))
datafObjs = datafObjs.sort_values(by ='b', axis=1)

print("Now the sorted Dataframe on the basis of single row index label 'b' : ")
print(datafObjs)

Output :
Now the sorted dataframe is sorted on the basis of row index label 'b' :
    0   2   1
a  11  33   2
b   4   6  55
c  77  99   8

Sorting columns of a Dataframe in Descending Order based on a single row :

Here we will sort columns of the dataframe in descending order based on single row.

Here we will sort columns of a Dataframe on basis of multiple rows by passing value in ascending as false.

#Program :

import pandas as sc

# List of Tuples as matrix
matrix = [(11, 2, 33),
          (4, 55, 6),
          (77, 8, 99),
          ]

# To create a DataFrame object like a Matrix
datafObjs = sc.DataFrame(matrix, index=list('abc'))

# Sorting the columns of a dataframe in descending order based on a single row with index label 'b'
datafObjs = datafObjs.sort_values(by='b', axis=1, ascending=False)

print("Now the sorted Dataframe on the basis of single row index label 'b' in descending order :")
print(datafObjs)

Output :
Now the sorted Dataframe on the basis of single row index label 'b' in descending order :

    1   2   0
a   2  33  11
b  55   6   4
c   8  99  77

Sorting columns of a Dataframe based on a multiple rows :

Here the columns of dataframe are sorted on the basis of multiple rows with passing labels ‘b’ & ‘c’ and axis=1.

#program :

import pandas as sc

# List of Tuples as matrix
matrix = [(11, 2, 33),
          (4, 55, 6),
          (77, 8, 99),
          ]

# To create a DataFrame object like a Matrix
datafObjs = sc.DataFrame(matrix, index=list('abc'))

# Sorting columns of the dataframe based on a multiple row with index labels 'b' & 'c'
datafObjs = datafObjs.sort_values(by =['b' , 'c' ], axis=1)

print("Now the sorted Dataframe based on multiple rows index label 'b' & 'c' :")
print(datafObjs)

Output :
Now the sorted Dataframe based on multiple rows index label 'b' & 'c' :

    0   2   1
a  11  33   2
b   4   6  55
c  77  99   8

Want to expert in the python programming language? Exploring Python Data Analysis using Pandas tutorial changes your knowledge from basic to advance level in python concepts.

Read more Articles on Python Data Analysis Using Padas – Modify a Dataframe

Pandas: Sort rows or columns in Dataframe based on values using Dataframe.sort_values() Read More »

Python Numpy : Select elements or indices by conditions from Numpy Array

Python / By Satyabrata Jena

How to elect elements or indices by conditions from Numpy Array in Python ?

In this article we will see how we can select and print elements from a given Numpy array provided with multiple conditions.

Selecting elements from a Numpy array based on Single or Multiple Conditions :

When we apply a comparison operator to a numpy array it is applied to each & every elements of the array. It is seen that True or False will be returned as bool array accordingly when its elements satisfies the condition.

#Program :

import numpy as sc

# To create a numpy array from 2 to 25 with interval 23
num_arr = sc.arange(2, 25, 3)

# To compare with all elements in array
bool_arr = num_arr < 15
print(bool_arr)

Output :
[ True  True  True  True  True False False False]

If we pass the resulted bool Numpy array in [] operartor then it will form a new Numpy Array with elements which were found True in corresponding bool Numpy array.

#Program :

import numpy as sc

num_arr = sc.arange(2, 25, 3)
bool_arr = num_arr < 15
print(bool_arr)

# Those elements will be selected where it is True at corresponding value in bool array
new_arr = num_arr[bool_arr]
print(new_arr)

Output :
[ True  True  True  True  True False False False]
[ 2  5  8 11 14]

Select elements from Numpy Array which are divisible by 5 :

We can select and print those elements which are divisible by 5 from given Numpy array.

#Program :

import numpy as sc

# Numpy arrray with elements frrm 3 to 25
num_arr = sc.arange(3, 25, 1)

# To select those numbers which are divisible by 5
new_arr = num_arr[num_arr%5==0]

print(new_arr)

Output :
[ 5  10  15  20 ]

Select elements from Numpy Array which are greater than 10 and less than 18 :

We can select and print those elements which are smaller than 10 and greater than 18 from given Numpy array.

#Program :

import numpy as sc

# Numpy arrray with elements frrm 3 to 25
num_arr = sc.arange(3, 25, 1)

# To select those numbers which are greater than 10 and smaller than 18
new_arr = num_arr[(num_arr > 10) & (num_arr < 18)]

print(new_arr)

Output :
[11 12 13 14 15 16 17]

Python Numpy : Select elements or indices by conditions from Numpy Array Read More »

Python : Create boolean Numpy array with all True or all False or random boolean values

Python / By Satyabrata Jena

How to create boolean Numpy array with all True or all False or random boolean values in Python ?

This article is about creating boolean Numpy array with all True or all False or random boolean values. Here we will discuss various ways of creating boolean Numpy array. So let’s start the topic.

Approach-1 : Creating 1-D boolean Numpy array with random boolean values

Python’s Numpy module provide random.choice( ) function which will create a boolean Numpy array with some random values.

Syntax : numpy.random.choice(a, size=None, replace=True, p=None)

where,

a: A Numpy array from which random values will be generated
size: Shape of array that is to b generated
replace: Here the sample is with or without replacement

To create a 1-D Numpy array with random true and false values, we will initially take a bool array and pass the array to numpy.random.choice() with some size which will generate a array with random true and false values.