If we have a numeric column in an R data frame and the unique number of values in the column is low that means the numerical column can be treated as a factor. Therefore, we can convert numeric columns to factor. To do this using dplyr package, we can use mutate_if function of dplyr package.Loading dplyr package and converting numerical columns in BOD data set (available in base R) to factor columns −Examplelibrary(dplyr) str(BOD) 'data.frame': 6 obs. of 2 variables: $ Time : num 1 2 3 4 5 7 $ demand: num 8.3 10.3 19 16 15.6 19.8 - ... Read More

Assume, you have a dataframe and the result for trim of minimum and the maximum threshold value, minimum threshold:    Column1 Column2 0    30    30 1    34    30 2    56    30 3    78    50 4    30    90 maximum threshold:    Column1 Column2 0    12    23 1    34    30 2    50    25 3    50    50 4    28    50 clipped dataframe is:    Column1 Column2 0    30    30 1    34    30 2    50    30 3   ... Read More

Assume, you have a dataframe and the result for quantify shape of a distribution is, kurtosis is: Column1    -1.526243 Column2     1.948382 dtype: float64 asymmetry distribution - skewness is: Column1    -0.280389 Column2     1.309355 dtype: float64SolutionTo solve this, we will follow the steps given below −Define a dataframeApply df.kurt(axis=0) to calculate the shape of distribution, df.kurt(axis=0)Apply df.skew(axis=0) to calculate unbiased skew over axis-0 to find asymmetry distribution, df.skew(axis=0)ExampleLet’s see the following code to get a better understanding −import pandas as pd data = {"Column1":[12, 34, 56, 78, 90],          "Column2":[23, 30, 45, ... Read More

SolutionAssume you have a dataframe and mean absolute deviation of rows and column is, mad of columns: Column1    0.938776 Column2    0.600000 dtype: float64 mad of rows: 0    0.500 1    0.900 2    0.650 3    0.900 4    0.750 5    0.575 6    1.325 dtype: float64To solve this, we will follow the steps given below −Define a dataframeCalculate mean absolute deviation of row as, df.mad()Calculate mean absolute deviation of row as, df.mad(axis=1)ExampleLet’s see the following code to get a better understanding −import pandas as pd data = {"Column1":[6, 5.3, 5.9, 7.8, 7.6, 7.45, 7.75], ... Read More

Assume, you have Panel and the average of the first row is, Average of first row is: Column1    0.274124 dtype: float64SolutionTo solve this, we will follow the steps given below −Set data value as dictionary key is ‘Column1’ with value as pd.DataFrame(np.random.randn(5, 3))data = {'Column1' : pd.DataFrame(np.random.randn(5, 3))}Assign data to Panel and save it as pp = pd.Panel(data)Print the column using dict key Column1print(p['Column1'])Calculate theAverage of first row using, major_xs(0) ,p.major_xs(0).mean()ExampleLet’s see the following code to get a better understanding −import pandas as pd import numpy as np data = {'Column1' : pd.DataFrame(np.random.randn(5, 3))} p = pd.Panel(data) print("Panel values:") ... Read More

SolutionAssume, you have a dataframe and minimum rank of a particular column,  Id Name    Age    Rank 0 1 Adam    12    1.0 1 2 David   13    3.0 2 3 Michael 14    5.0 3 4 Peter   12    1.0 4 5 William 13    3.0To solve this, we will follow the steps given below −Define a dataframe.Assign df[‘Age’] column inside rank function to calculate the minimum rank for axis 0 is, df["Age"].rank(axis=0, method ='min', ascending=True)ExampleLet’s see the following code to get a better understanding −import pandas as pd data = {'Id': [1, 2, 3, ... Read More

The result for a maximum value of the first column in panel ismaximum value of first column is ; Column1    1.377292SolutionTo solve this, we will follow the below approach −Set data value as dictionary key is ‘Column1’ with value as pd.DataFrame(np.random.randn(5, 3))data = {'Column1' : pd.DataFrame(np.random.randn(5, 3))}Assign data to Panel and save it as pp = pd.Panel(data)Print the column using dict key Column1print(p['Column1'])Calculate the maximum value of first column using, minor_xs(0) ,p.minor_xs(0).max()ExampleLet’s see the following code to get a better understanding −import pandas as pd import numpy as np data = {'Column1' : pd.DataFrame(np.random.randn(5, 3))} p = pd.Panel(data) print("Panel ... Read More

Assume, you have a dataframe and the shift index by two periods in positive and negative direction is, shift the index by three periods in positive direction                      Id Age 2020-01-01 00:00:00 NaN NaN 2020-01-01 12:00:00 NaN NaN 2020-01-02 00:00:00 1.0 10.0 2020-01-02 12:00:00 2.0 12.0 2020-01-03 00:00:00 3.0 14.0 shift the index by three periods in negative direction                      Id Age 2020-01-01 00:00:00 3.0 14.0 2020-01-01 12:00:00 4.0 11.0 2020-01-02 00:00:00 5.0 13.0 2020-01-02 12:00:00 NaN NaN 2020-01-03 00:00:00 NaN NaNSolutionTo ... Read More

Assume, you have a dataframe and the result for removing first duplicate rows are,     Id Age 0    1 12 3    4 13 4    5 14 5    6 12 6    2 13 7    7 16 8    3 14 9    9 15 10  10 14SolutionTo solve this, we will follow the steps given below −Define a dataframeApply drop_duplicates function inside Id and Age column then assign keep initial value as ‘last’.df.drop_duplicates(subset=['Id', 'Age'], keep='last')Store the result inside same dataframe and print itExampleLet’s see the below implementation to get a better understanding −import pandas ... Read More

Assume, you have a dataframe and the result for calculating covariance from grouped data and corresponding column as, Grouped data covariance is:                   mark1       mark2 subjects maths    mark1    25.0    12.500000          mark2    12.5    108.333333 science  mark1    28.0    50.000000          mark2    50.0    233.333333 Grouped data covariance between two columns: subjects maths    12.5 science  50.0 dtype: float64SolutionTo solve this, we will follow the steps given below −Define a dataframeApply groupby function inside dataframe subjects ... Read More