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Assume, you have a dataframe and the result for flatten records in C and F order as, flat c_order: [10 12 25 13 3 12 11 14 24 15 6 14] flat F_order: [10 25 3 11 24 6 12 13 12 14 15 14]SolutionTo solve this, we will follow the steps given below −Define a dataframeApply df.values.ravel() function inside set an argument as order=’C’ and save it as C_order, C_order = df.values.ravel(order='C')Apply df.values.ravel() function inside set an argument as order=’F’ and save it as F_order, F_order = df.values.ravel(order='F')ExampleLet’s check the following code to get a better understanding ... Read More
Assume, you have a dataframe and the result for orderDict with list of tuples are −OrderedDict([('Index', 0), ('Name', 'Raj'), ('Age', 13), ('City', 'Chennai'), ('Mark', 80)]) OrderedDict([('Index', 1), ('Name', 'Ravi'), ('Age', 12), ('City', 'Delhi'), ('Mark', 90)]) OrderedDict([('Index', 2), ('Name', 'Ram'), ('Age', 13), ('City', 'Chennai'), ('Mark', 95)])SolutionTo solve this, we will follow the steps given below −Define a dataframeSet for loop to access all the rows using df.itertuples() function inside set name=’stud’for row in df.itertuples(name='stud')Convert all the rows to orderDict with list of tuples using rows._asdict() function and save it as dict_row. Finally print the values, dict_row = row._asdict() print(dict_row)ExampleLet’s check the ... Read More
Assume, you have a dataframe and the result for adjusted and non-adjusted EWM are −adjusted ewm: Id Age 0 1.000000 12.000000 1 1.750000 12.750000 2 2.615385 12.230769 3 2.615385 13.425000 4 4.670213 14.479339 non adjusted ewm: Id Age 0 1.000000 12.000000 1 1.666667 12.666667 2 2.555556 12.222222 3 2.555556 13.407407 4 4.650794 14.469136SolutionTo solve this, we will follow the steps given below −Define a dataframeCalculate adjusted ewm with delay 0.5 using df.ewm(com=0.5).mean().df.ewm(com=0.5).mean()Calculate non-adjusted ewm with delay 0.5 using df.ewm(com=0.5).mean().df.ewm(com=0.5, adjust=False).mean()Exampleimport numpy as np import pandas as pd df ... Read More
SolutionTo solve this, we will follow the steps given below −Define a dataframeApply df.interpolate funtion inside method =’linear’, limit_direction =’forward’ and fill NaN limit = 2df.interpolate(method ='linear', limit_direction ='forward', limit = 2Exampleimport pandas as pd df = pd.DataFrame({"Id":[1, 2, 3, None, 5], "Age":[12, 12, 14, 13, None], "Mark":[80, 90, None, 95, 85], }) print("Dataframe is:",df) print("Interpolate missing values:") print(df.interpolate(method ='linear', limit_direction ='forward', limit = 2))OutputDataframe is: Id Age Mark 0 1.0 12.0 80.0 1 2.0 12.0 90.0 2 3.0 14.0 NaN 3 NaN 13.0 95.0 4 5.0 NaN 85.0 Interpolate missing values: Id Age Mark 0 1.0 12.0 80.0 1 2.0 12.0 90.0 2 3.0 14.0 92.5 3 4.0 13.0 95.0 4 5.0 13.0 85.0
Assume, you have a dataframe and the result for renaming the axis is,Rename index: index Id Age Mark 0 1.0 12.0 80.0 1 2.0 12.0 90.0 2 3.0 14.0 NaN 3 NaN 13.0 95.0 4 5.0 NaN 85.0SolutionTo solve this, we will follow the steps given below −Define a dataframeApply df.rename_axis() function inside axis name as ‘index’ and set axis=1df.rename_axis('index',axis=1)Exampleimport pandas as pd df = pd.DataFrame({"Id":[1, 2, 3, None, 5], "Age":[12, 12, 14, 13, None], "Mark":[80, 90, None, 95, 85], }) print("Dataframe is:",df) print("Rename index:") df = df.rename_axis('index',axis=1) print(df)OutputDataframe is: Id Age Mark 0 1.0 12.0 80.0 1 2.0 12.0 90.0 2 3.0 14.0 NaN 3 NaN 13.0 95.0 4 5.0 NaN 85.0 Rename index: index Id Age Mark 0 1.0 12.0 80.0 1 2.0 12.0 90.0 2 3.0 14.0 NaN 3 NaN 13.0 95.0 4 5.0 NaN 85.0
Assume you have two dataframes and the result for cross-tabulation is,Age 12 13 14 Mark 80 90 85 Id 1 1 0 0 2 0 1 0 3 1 0 0 4 0 1 0 5 0 0 1SolutionTo solve this, we will follow the steps given below −Define two dataframesApply df.crosstab() function inside index as ‘Id’ and columns as ‘Age’ and ‘Mark’. It is defined below,pd.crosstab(index=df['Id'],columns=[df['Age'],df1['Mark']])Exampleimport pandas as pd df = pd.DataFrame({'Id':[1,2,3,4,5],'Age':[12,13,12,13,14]}) df1 = pd.DataFrame({'Mark':[80,90,80,90,85]}) print(pd.crosstab(index=df['Id'],columns=[df['Age'],df1['Mark']]))OutputAge 12 13 14 Mark 80 90 85 Id 1 1 0 0 2 0 1 0 3 1 0 0 4 0 1 0 5 0 0 1
The result for the length of elements in all column in a dataframe is, Dataframe is: Fruits City 0 Apple Shimla 1 Orange Sydney 2 Mango Lucknow 3 Kiwi Wellington Length of the elements in all columns Fruits City 0 5 6 1 6 6 2 5 7 3 4 10SolutionTo solve this, we will follow the steps given below −Define a dataframeUse df.applymap function inside lambda function to calculate the length of elements in all column asdf.applymap(lambda x:len(str(x)))ExampleLet’s check the following code to get ... Read More
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