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Assume, you have a dataframe, one two three 0 1 2 3 1 4 5 6And the result for removing single column is, two three 0 2 3 1 5 6The result for removing after more than one column is, three 0 3 1 6To solve this, we will follow the steps given below −Solution 1Define a dataframeDelete a particular column using below method, del df['one']ExampleLet’s see the below code to get a better understanding −import pandas as pd data = [[1, 2, 3], [4, 5, 6]] df = pd.DataFrame(data, columns=('one', 'two', 'three')) print("Before ... Read More
Assume, you have the following series, Series is: 0 1 1 22 2 3 3 4 4 22 5 5 6 22And the result for the most repeated element is, Repeated element is: 22SolutionTo solve this, we will follow the below approach, Define a seriesSet initial count is 0 and max_count value as series first element value data[0]count = 0 max_count = data[0]Create for loop to access series data and set frequency_count as l.count(i)for i in data: frequency_count = l.count(i)Set if condition to compare with max_count value, if the condition is true ... Read More
Assume, you have a dataframe, DataFrame is: id mark age 0 1 70 12 1 2 60 13 2 3 40 12 3 4 50 13 4 5 80 12 5 6 90 13 6 7 60 12And, the result for selecting any random odd index row is, Random odd index row is: id 4 mark 50 age 13SolutionTo solve this, we will follow the steps given below −Define a dataframeCreate an empty list to append odd index valuesCreate a for loop to access all the index. It is defined ... Read More
Assume, you have two dataframe, first dataframe is id country 0 1 India 1 2 UK 2 3 US 3 4 China second dataframe is id City 0 1 Chennai 1 11 Cambridge 2 22 Chicago 3 4 ChengduAnd the result for merging based on same column is, Merging data based on same column - id id country City 0 1 India Chennai 1 4 China ChengduSolutionTo solve this, we will follow the steps given below −Define a two dataframesMerge two dataframes based on the same column id is defined below, pd.merge(first_df, ... Read More
Assume, you have a dataframe, col1 col2 0 o e 1 e e 2 i u 3 e o 4 i i 5 u o 6 e a 7 u o 8 a u 9 e aThe result for matched index and count is, index is col1 col2 1 e e 4 i i count is 2SolutionTo solve this, we will follow the steps given below −Define a dataframeCompare first and second matching index values using the below method, df.iloc[np.where(df.col1==df.col2)])Find the total count of matched columns using the ... Read More
Assume, you have a dataframe0 1 20 10 20 30 1 40 50 60 2 70 80 90The result for replaced 1 by diagonal of a dataframe is −0 1 2 0 1 20 30 1 40 1 60 2 70 80 1SolutionTo solve this, we will follow the steps given below −Define a dataframeCreate nested for loop to access all rows and columns, for i in range(len(df)): for j in range(len(df)):Check if the condition to match the diagonals, if it is matched then replace the position by 1. It is defined below, if i == j: df.iloc[i ... Read More
Assume you have a dataframe, one two three 0 12 13 5 1 10 6 4 2 16 18 20 3 11 15 58The result for storing the minimum value in new row and column is −Add new column to store min value one two three min_value 0 12 13 5 5 1 10 6 4 4 2 16 18 20 16 3 11 15 58 11 Add new row to store min value one two three min_value 0 ... Read More
Assume you have a sqlite3 database with student records and the result for reading all the data is, Id Name 0 1 stud1 1 2 stud2 2 3 stud3 3 4 stud4 4 5 stud5SolutionTo solve this, we will follow the steps given below −Define a new connection. It is shown below, con = sqlite3.connect("db.sqlite3")Read sql data from the database using below function, pd.read_sql_query()Select all student data from table using read_sql_query with connection, pd.read_sql_query("SELECT * FROM student", con)ExampleLet us see the complete implementation to get a better understanding −import pandas as pd import sqlite3 con = sqlite3.connect("db.sqlite3") df = ... Read More
Assume you have a series and the result for Boolean operations, And operation is: 0 True 1 True 2 False dtype: bool Or operation is: 0 True 1 True 2 True dtype: bool Xor operation is: 0 False 1 False 2 True dtype: boolSolutionTo solve this, we will follow the below approach.Define a SeriesCreate a series with boolean and nan valuesPerform boolean True against bitwise & operation to each element in the series defined below, series_and = pd.Series([True, np.nan, False], dtype="bool") & TruePerform boolean True against bitwise | operation ... Read More
Input −Assume you have a DataFrame, and the result for transpose of index and columns are, Transposed DataFrame is 0 1 0 1 4 1 2 5 2 3 6Solution 1Define a DataFrameSet nested list comprehension to iterate each element in the two-dimensional list data and store it in result.result = [[data[i][j] for i in range(len(data))] for j in range(len(data[0]))Convert the result to DataFrame, df2 = pd.DataFrame(result)ExampleLet us see the complete implementation to get a better understanding −import pandas as pd data = [[1, 2, 3], [4, 5, 6]] df = pd.DataFrame(data) print("Original DataFrame is", df) result = [[data[i][j] ... Read More