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Sorting dataframe by columns which contains nan values. DataFrame has "sort_values()" method can take an another parameter called "na_position".Using this parameter the rows containing nan values can be pushed to either top or bottom Creating a new dataframe with dictionary  # importing pandas import pandas as pd import numpy as np # animal_data dictionary animal_data = { "Name": ["Cat", "Dog", "Cow"], "Speed": [15, 12, 10], "Sound": ["Meow", "Woof", "Mooo"], "Rank": [1, 5, 3], "Jumping_height": [20, 10, np.NaN], } # creating a dataframe using the animal_data dictionary animal_df = pd.DataFrame(animal_data) # printing animal_df print("animal_df \n", animal_df) animal_df Name Speed Sound Rank Jumping_height 0 Cat 15 Meow 1 20.0 1 Dog 12 Woof 5 10.0 2 Cow 10 Mooo ...

Sorting pandas dataframe by single or multiple columns. DataFrame has "sort_values()" method which can be used to sort the dataframe based single or multiple columns , control sorting flow and choose ascending or descending order. Creating a new dataframe with dictionary  # importing pandas import pandas as pd import numpy as np # animal_data dictionary animal_data = { "Name": ["Cat", "Dog", "Cow"], "Speed": [15, 12, 10], "Sound": ["Meow", "Woof", "Mooo"], "Rank": [1, 5, 3], "Jumping_height": [20, 10, np.NaN], } # creating a dataframe using the animal_data dictionary animal_df = pd.DataFrame(animal_data) # printing animal_df print("animal_df \n", animal_df) animal_df Name Speed Sound Rank Jumping_height 0 Cat 15 Meow 1 20.0 1 Dog 12 Woof 5 10.0 2 Cow 10 Mooo 3 ...

Find and replace string values in a column - pandas dataframe String type has str.replace() method which can used for finding and replacing values.We are required to chain this method string type values and pass parameters for value to be searched and new replacing value. Based on the dataset one might be required to run search and replace on columns with mixed datatypes ie. int ,etc. To handle this assert type as string and then chain required string methods Creating a new dataframe with dictionary  # importing pandas import pandas as pd # animal_sp_char_df - with special characters animal_data_with_sp_char = { "Name": ["Cat", "Dog", "Cow", "Tiger", "Goat", "Snake"], "Sound": ["#Meow###", "Wo##of", "Mo#oo", "Rwaar###", "##Baaa", "Skkk##sss"], "Mixed": [123, "#13", "53###", 321, "###456", ...

Selecting every nth row from the dataframe. We can select every nth row item from the pandas dataframe by using ".iloc" method. It has the slicing features and stepping features similar to list slicing. iloc is index based and starts from zero Creating a new dataframe with dictionary  # importing pandas import pandas as pd # animal_data_ animal_data_ext = { "Name": ["Cat", "Dog", "Cow","Tiger","Goat","Snake"], "Sound": ["Meow", "Woof", "Mooo","Rwaar","Baaa","Skkksss"], } #creating a dataframe using the animal_data_ext dictionary animal_ext_df = pd.DataFrame(animal_data_ext) #printing animal_ext_df print("animal_ext_df \n", animal_ext_df) animal_ext_df Name Sound 0 Cat Meow 1 Dog Woof 2 Cow Mooo 3 Tiger Rwaar 4 Goat Baaa 5 Snake Skkksss Selecting every nth row (includi...

Lowercase / uppercase all column cell contents. String type has methods for lower-casing and upper-casing. A column is selected from dataframe and str (string) operations are made on it. But this would throw errors for int data types. We can either skip over those columns using conditionals or use "astype" assert it as a string and then operate string methods on it without any error.Based on requirement one might choose to either skip over or handle all columns with mixed type contents . Creating a new dataframe with dictionary (which has alpha numeric cell contents) # importing pandas import pandas as pd # animal_data_with_alpha_nums dictionary animal_data_with_alpha_nums = { "Name": ["Cat", "Dog", "Cow"], "Speed": [15, 12, 10], "Sound": ["Meow", "Woof", "Mooo"], "Alpha_Num":[123,"aBc123XyZ","123xYz"] } #creating a dataframe using the a...

Generating column unique values from all columns from a dataframe and storing it into python dictionary. Dataframe has "unique" method which returns the required unique values.  Dataframe "columns" property will also be used to get the column names which will iterated over for the purpose. Creating a new dataframe with dictionary (which has duplicate data) # importing pandas import pandas as pd # animal_data_with_duplicates dictionary animal_data_with_duplicates = { "Name": ["Cat", "Dog", "Cow","Tiger","Cat"], "Speed": [15, 12, 10,20,15], "Sound": ["Meow", "Woof", "Mooo","Roar","Meow"], } #creating a dataframe using the animal_data_with_duplicates dictionary animal_with_duplicates_df = pd.DataFrame(animal_data_with_duplicates) #printing animal_with_duplicates_df print("animal_with_duplicates_df \n", animal_with_...

Generating column unique values from a dataframe. Dataframe has "unique" method which returns the required unique values. Creating a new dataframe with dictionary (which has duplicate data) # importing pandas import pandas as pd # animal_data_with_duplicates dictionary animal_data_with_duplicates = { "Name": ["Cat", "Dog", "Cow","Tiger","Cat"], "Speed": [15, 12, 10,20,15], "Sound": ["Meow", "Woof", "Mooo","Roar","Meow"], } #creating a dataframe using the animal_data_with_duplicates dictionary animal_with_duplicates_df = pd.DataFrame(animal_data_with_duplicates) #printing animal_with_duplicates_df print("animal_with_duplicates_df \n", animal_with_duplicates_df) animal_with_duplicates_df Name Speed Sound 0 Cat 15 Meow 1 Dog 12 Woof 2 Cow 10 Mooo 3 Tiger 20 Roar 4 Cat 15 Meow #g...

Generating index list from a dataframe. Dataframe object has "index" method which returns the required data object. This can be converted into list. Index list can be used for counting of current number of rows , for using ".iloc" , shuffling etc. Creating a new dataframe with data # importing pandas import pandas as pd # animal_data dictionary animal_data = { "Name": ["Cat", "Dog", "Cow"], "Speed": [15, 12, 10], "Sound": ["Meow", "Woof", "Mooo"], } #creating a dataframe using the animal_data dictionary animal_df = pd.DataFrame(animal_data) #printing animal_df print("animal_df \n", animal_df) animal_df Name Speed Sound 0 Cat 15 Meow 1 Dog 12 Woof 2 Cow 10 Mooo # create a list containing values indices_as_list = list(animal_df.index) print("indices_as_list \n", indices_as_list) print("\n length of indices_as_...

Generating column names list from a dataframe. Dataframe object has "columns" method which returns the required data object. This can be converted into list.  Creating a new dataframe with data # importing pandas import pandas as pd # animal_data dictionary animal_data = { "Name": ["Cat", "Dog", "Cow"], "Speed": [15, 12, 10], "Sound": ["Meow", "Woof", "Mooo"], } #creating a dataframe using the animal_data dictionary animal_df = pd.DataFrame(animal_data) #printing animal_df print("animal_df \n", animal_df) animal_df Name Speed Sound 0 Cat 15 Meow 1 Dog 12 Woof 2 Cow 10 Mooo # create a list containing values column_names_as_list = list(animal_df.columns) print("column_names_as_list \n", column_names_as_list) print("\n length of column_names_as_list \n", len(column_names_as_list)) #or directly use the values a...

Creating a pandas dataframe with dictionary data. A dictionary containing lists as values can directly passed into pd.Dataframe method to create a new dataframe using the passed data. The keys in the dictionary will be used as the column headers and values in the list will be used as row values. # importing pandas import pandas as pd # animal_data dictionary animal_data = { "Name": ["Cat", "Dog", "Cow"], "Speed": [15, 12, 10], "Sound": ["Meow", "Woof", "Mooo"], } #creating a dataframe using the animal_data dictionary animal_df = pd.DataFrame(animal_data) #printing animal_df print("animal_df \n", animal_df) animal_df Name Speed Sound 0 Cat 15 Meow 1 Dog 12 Woof 2 Cow 10 Mooo

TypeDoc makes building documentations very easy. It can fit into most codebases with minimal setup. TypeDoc extracts & builds documentation from typescript types , interfaces,.. and also makes use javascript doc comments. Install dependencies: npm i typedoc -d yarn add typedoc -d //installing as dev dependency After installation , create a "typedoc.json" in the root path. Find more resources here  https://typedoc.org/options/configuration/ Configuration file I have customised  https://github.com/808vita/nextjs-blog-with-auth/blob/deploy/typedoc.json Repository GitHub link :  Full repository github - 808vita -oofdev