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Assume, you have dataframe and the result for percentage change between Id and Age columns top 2 and bottom 2 valueId and Age-top 2 values Id Age 0 NaN NaN 1 1.0 0.0 Id and Age-bottom 2 values Id Age 3 0.000000 -0.071429 4 0.666667 0.000000SolutionTo solve this, we will follow the steps given below −Define a dataframeApply df[[‘Id’, ’Age’]].pct_change() inside slicing [0:2]df[['Id', 'Age']].pct_change()[0:2]Apply df[[‘Id’, ’Age’]].pct_change() inside slicing [-2:]df[['Id', 'Age']].pct_change()[0:2]ExampleLet’s check the following code to get a better understanding −import pandas as pd df = pd.DataFrame({"Id":[1, 2, 3, None, 5], ... Read More
Assume, you have a dataframe and the result for table-wise function is, Table wise function: Id Mark 0 6.0 85.0 1 7.0 95.0 2 8.0 75.0 3 9.0 90.0 4 10.0 95.0SolutionTo solve this, we will follow the steps given below −Define a dataframeCreate a user-defined function avg with two arguments and return the result as (a+b/2). It is defined below, def avg(a, b): return (a+b/2)Apply pipe() function to perform table-wise function inside first value as avg() and the second argument as 10 to calculate the avg of all the dataframe values.df.pipe(avg, 10)ExampleLet’s check the following code to ... 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