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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
Input −Assume you have a series and default float quantilevalue is 3.0SolutionTo solve this, we will follow the steps given below −Define a SeriesAssign quantile default value .5 to the series and calculate the result. It is defined below,data.quantile(.5) ExampleLet us see the complete implementation to get a better understanding −import pandas as pd l = [10,20,30,40,50] data = pd.Series(l) print(data.quantile(.5))Output30.0
Input −Assume, we have a DataFrame and group the records based on the designation is −Designation architect 1 programmer 2 scientist 2SolutionTo solve this, we will follow the below approaches.Define a DataFrameApply groupby method for Designation column and calculate the count as defined below,df.groupby(['Designation']).count()ExampleLet us see the following implementation to get a better understanding.import pandas as pd data = { 'Id':[1,2,3,4,5], 'Designation': ['architect','scientist','programmer','scientist','programmer']} df = pd.DataFrame(data) print("DataFrame is",df) print("groupby based on designation:") print(df.groupby(['Designation']).count())OutputDesignation architect 1 programmer 2 scientist 2
Input −Assume, we have DataFrame with City and State columns and find the city, state name startswith ‘k’ and store into another CSV file as shown below −City, State Kochi, KeralaSolutionTo solve this, we will follow the steps given below.Define a DataFrameCheck the city starts with ‘k’ as defined below, df[df['City'].str.startswith('K') & df['State'].str.startswith('K')] Finally, store the data in the ‘CSV’ file as below, df1.to_csv(‘test.csv’)ExampleLet us see the following implementation to get a better understanding.import pandas as pd import random as r data = { 'City': ['Chennai', 'Kochi', 'Kolkata'], 'State': ['Tamilnad', 'Kerala', 'WestBengal']} df = pd.DataFrame(data) print("DataFrame is", df) df1 = ... Read More
Input −Assume, sample DataFrame is, Id Name 0 1 Adam 1 2 Michael 2 3 David 3 4 Jack 4 5 PeterOutputput −Random row is Id 5 Name PeterSolutionTo solve this, we will follow the below approaches.Define a DataFrameCalculate the number of rows using df.shape[0] and assign to rows variable.set random_row value from randrange method as shown below.random_row = r.randrange(rows)Apply random_row inside iloc slicing to generate any random row in a DataFrame. It is defined below, df.iloc[random_row, :]ExampleLet us see the following implementation to get a better understanding.import pandas as pd import random as r data = { ... Read More
Input −Assume, sample DataFrame is, Id Name 0 1 Adam 1 2 Michael 2 3 David 3 4 Jack 4 5 PeterOutput −After, sorting the elements in descending order as, Id Name 4 5 Peter 1 2 Michael 3 4 Jack 2 3 David 0 1 AdamSolutionTo solve this, we will follow the below approaches.Define a DataFrameApply DataFrame sort_values method based on Name column and add argument ascending=False to show the data in descending order. It is defined below, df.sort_values(by='Name', ascending=False)ExampleLet us see the following implementation to get a better understanding.import pandas as pd data = {'Id': [1, ... Read More
Input −Assume, sample DataFrame is, Id Age salary 0 1 27 40000 1 2 22 25000 2 3 25 40000 3 4 23 35000 4 5 24 30000 5 6 32 30000 6 7 30 50000 7 8 28 20000 8 9 29 32000 9 10 27 23000Output −Result for mean and product of given slicing rows are, mean is Age 23.333333 salary 33333.333333 product is Age 12650 salary 35000000000000SolutionTo solve this, we will follow the below approaches.Define ... Read More
Input −Assume, you have a DataFrame, Id Age 0 1 21 1 2 23 2 3 32 3 4 35 4 5 18Output −Total number of age between 20 to 30 is 2.SolutionTo solve this, we will follow the below approaches.Define a DataFrameSet the DataFrame Age column between 20,30. Store it in result DataFrame. It is defined below,df[df['Age'].between(20,30)]Finally, calculate the length of the result.ExampleLet us see the following implementation to get a better understanding.import pandas as pd data = {'Id':[1,2,3,4,5],'Age':[21,23,32,35,18]} df = pd.DataFrame(data) print(df) print("Count the age between 20 to 30") result = df[df['Age'].between(20,30)] print(len(result))Output Id Age 0 1 21 1 2 23 2 3 32 3 4 35 4 5 18 Count the age between 20 to 30 2
Input −Assume, you have DataFrame, Id Name Grade 0 1 stud1 A 1 2 stud2 B 2 3 stud3 C 3 4 stud4 A 4 5 stud5 AOutput −And the result for ‘A’ grade students name, 0 stud1 3 stud4 4 stud5SolutionTo solve this, we will follow the below approaches.Define a DataFrameCompare the value to the DataFramedf[df['Grade']=='A']Store the result in another DataFrame and fetch Name.ExampleLet us see the following implementation to get a better understanding.import pandas as pd data = [[1, 'stud1', 'A'], [2, 'stud2', 'B'], [3, 'stud3', 'C'], [4, 'stud4', 'A'], ... Read More