Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
-
Economics & Finance
Articles by Vani Nalliappan
Page 5 of 13
Write a program in Python to perform average of rolling window size 3 calculation in a given dataframe
Assume, you have a dataframe and the result for rolling window size 3 calculation is, Average of rolling window is: Id Age Mark 0 NaN NaN NaN 1 1.5 12.0 85.0 2 2.5 13.0 80.0 3 3.5 13.5 82.5 4 4.5 31.5 90.0 5 5.5 60.0 87.5To solve this, we will follow the below approach −SolutionDefine a dataframeApply df.rolling(window=2).mean() to calculate average of rolling window size 3 isdf.rolling(window=2).mean()ExampleLet’s check the following code to get a better understanding −import pandas as pd df = pd.DataFrame({"Id":[1, 2, 3, 4, 5, 6], ...
Read MoreWrite a program in Python to slice substrings from each element in a given series
Assume, you have a series and the result for slicing substrings from each element in series as, 0 Ap 1 Oa 2 Mn 3 KwTo solve this, we will follow the below approaches −Solution 1Define a seriesApply str.slice function inside start=0, stop-4 and step=2 to slice the substring from the series.data.str.slice(start=0, stop=4, step=2)ExampleLet’s check the following code to get a better understanding −import pandas as pd data = pd.Series(['Apple', 'Orange', 'Mango', 'Kiwis']) print(data.str.slice(start=0, stop=4, step=2))Output0 Ap 1 Oa 2 Mn 3 KwSolution 2Define a seriesApply string index slice to start from 0 ...
Read MoreWrite a Python function to split the string based on delimiter and convert to series
The result for splitting the string with ’' delimiter and convert to series as, 0 apple 1 orange 2 mango 3 kiwiTo solve this, we will follow the below approach −Solution 1define a function split_str() which accepts two arguments string and delimiterCreate s.split() function inside delimiter value and store it as split_datasplit_data = s.split(d)Apply split_data inside pd.Series() to generate series data.pd.Series(split_data)Finally, call the function to return the result.ExampleLet’s check the following code to get a better understanding −import pandas as pd def split_str(s, d): split_data = s.split(d) print(pd.Series(split_data)) split_str('apple\torange\tmango\tkiwi', '\t')Output0 apple 1 ...
Read MoreWrite a program in Python to print the first and last three days from a given time series data
Assume, you have time series and the result for the first and last three days from the given series as, first three days: 2020-01-01 Chennai 2020-01-03 Delhi Freq: 2D, dtype: object last three days: 2020-01-07 Pune 2020-01-09 Kolkata Freq: 2D, dtype: objectTo solve this, we will follow the steps given below −SolutionDefine a series and store it as data.Apply pd.date_range() function inside start date as ‘2020-01-01’ and periods = 5, freq =’2D’ and save it as time_seriestime_series = pd.date_range('2020-01-01', periods = 5, freq ='2D')Set date.index = time_seriesPrint the first three days using data.first(’3D’) and save it ...
Read MoreWrite a program in Python to generate a random array of 30 elements from 1 to 100 and calculate maximum by minimum of each row in a dataframe
Result for generating dataframe maximum by a minimum of each row is0 43.000000 1 1.911111 2 2.405405 3 20.000000 4 7.727273 5 6.333333To solve this, we will follow the steps given below −Solution 1Define a dataframe with size of 30 random elements from 1 to 100 and reshape the array by (6, 5) to change 2-D arraydf = pd.DataFrame(np.random.randint(1, 100, 30).reshape(6, 5))Create df.apply function inside lambda method to calculate np.max(x)/np.min(x) with axis as 1 and save as max_of_min. It is defined below, max_of_min = df.apply(lambda x: np.max(x)/np.min(x), axis=1)Finally print the max_of_minExampleLet’s check the following ...
Read MoreWrite a program in Python to find which column has the minimum number of missing values in a given dataframe
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 MoreWrite a Python function to calculate the total number of business days from a range of start and end date
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 MoreWrite a program in Python to perform flatten the records in a given dataframe by C and F order
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 MoreWrite a program in Python to print dataframe rows as orderDict with a list of tuple values
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 MoreWrite a program in Python to caluculate the adjusted and non-adjusted EWM in a given dataframe
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