Assume, you have a dataframe and the minimum number of missing value column is, DataFrame is:    Id    Salary     Age 0 1.0    20000.0   22.0 1 2.0    NaN       23.0 2 3.0    50000.0   NaN 3 NaN    40000.0   25.0 4 5.0    80000.0   NaN 5 6.0    NaN       25.0 6 7.0    350000.0  26.0 7 8.0    55000.0   27.0 8 9.0    60000.0   NaN 9 10.0   70000.0   24.0 lowest missing value column is: IdTo solve this, we will follow the steps given ... Read More

Assume, you have a date_range of dates and the result for the total number of business days are, Dates are: DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-06',                '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10',                '2020-01-13', '2020-01-14', '2020-01-15', '2020-01-16',                '2020-01-17', '2020-01-20', '2020-01-21', '2020-01-22',                '2020-01-23', '2020-01-24', '2020-01-27', '2020-01-28',                '2020-01-29', '2020-01-30', '2020-01-31'],                dtype='datetime64[ns]', freq='B') Total number of days: 23Solution 1Define a function as business_days()set pd.bdate_range() function start ... Read More

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