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Vani Nalliappan has Published 130 Articles
Vani Nalliappan
304 Views
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 ... Read More
Vani Nalliappan
336 Views
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', ... Read More
Vani Nalliappan
104 Views
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 ... Read More
Vani Nalliappan
68 Views
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 ... Read More
Vani Nalliappan
111 Views
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 ... Read More
Vani Nalliappan
207 Views
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], ... Read More
Vani Nalliappan
181 Views
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 ... Read More
Vani Nalliappan
445 Views
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 ... Read More
Vani Nalliappan
270 Views
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 ... Read More
Vani Nalliappan
272 Views
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 ... Read More
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