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Assume, you have time series and the result for maximum month-end frequency, DataFrame is: Id time_series 0 1 2020-01-05 1 2 2020-01-12 2 3 2020-01-19 3 4 2020-01-26 4 5 2020-02-02 5 6 2020-02-09 6 7 2020-02-16 7 8 2020-02-23 8 9 2020-03-01 9 10 2020-03-08 Maximum month end frequency: Id time_series time_series 2020-01-31 4 2020-01-26 2020-02-29 8 2020-02-23 2020-03-31 10 2020-03-08SolutionTo solve this, we will follow the steps given below −Define a dataframe with one column, d = {'Id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]} ... Read More
Assume, we have already saved pandas.csv file and export the file to Html formatSolutionTo solve this, we will follow the steps given below −Read the csv file using the read_csv method as follows −df = pd.read_csv('pandas.csv')Create new file pandas.html in write mode using file object, f = open('pandas.html', 'w')Declare result variable to convert dataframe to html file format, result = df.to_html()Using the file object, write all the data from the result. Finally close the file object, f.write(result) f.close()ExampleLet’s see the below implementation to get a better understanding −import pandas as pd df = pd.read_csv('pandas.csv') print(df) f = open('pandas.html', 'w') result ... Read More
Assume, you have an Excel file stored with the name of pandas.xlsx in your location.SolutionTo solve this, we will follow the steps given below −Define pd.read_excel method to read data from pandas.xlsx file and save it as dfdf = pd.read_excel('pandas.xlsx')Apply df.iloc[:, 0] to print all rows of first columndf.iloc[:, 0]Apply df.iloc[:, -1] to print all rows of last columndf.iloc[:, -1]ExampleLet’s see the below implementation to get a better understanding −import pandas as pd df = pd.read_csv('products.csv') print("all rows of first column is") print(df.iloc[:, 0]) print("all rows of last column is") print(df.iloc[:, -1])Outputall rows of first column is 0 ... Read More
Assume you have the following data in your csv file and save it as pandas.csv.pandas.csvId, Data 1, 11 2, 22 3, 33 4, 44 5, 55 6, 66 7, 77 8, 88 9, 99 10, 100The result for sum of last two records as, Sum of last two rows: Id 9 Data 99Solution 1Access stored data from csv file and save it as data using the below method, data = pd.read_csv('pandas.csv')Convert the data into dataframe and store inside df, df = pd.DataFrame(data)Apply the below method to take last two records and calculate the sum, df.tail(2)).sum()ExampleLet’s see the below implementation ... Read More
Assume, you have a dataframe and the result for export dataframe to multiple sheets as, To solve this, we will follow the steps given below −Solutionimport xlsxwriter module to use excel conversionDefine a dataframe and assign to dfApply pd.ExcelWriter function inside name excel name you want to create and set engine as xlsxwriterexcel_writer = pd.ExcelWriter('pandas_df.xlsx', engine='xlsxwriter')Convert the dataframe to multiple excel sheets using the below method, df.to_excel(excel_writer, sheet_name='first_sheet') df.to_excel(excel_writer, sheet_name='second_sheet') df.to_excel(excel_writer, sheet_name='third_sheet')Finally save the excel_writerexcel_writer.save()ExampleLet’s understand the below code to get a better understanding −import pandas as pd import xlsxwriter df = pd.DataFrame({'Fruits': ["Apple", "Orange", "Mango", "Kiwi"], ... Read More
Assume you have a series and the result for separating alphabets and digits and store it in dataframe as, series is: 0 abx123 1 bcd25 2 cxy30 dtype: object Dataframe is 0 1 0 abx 123 1 bcd 25 2 cxy 30To solve this, we will follow the below approach, SolutionDefine a series.Apple series extract method inside use regular expression pattern to separate alphabets and digits then store it in a dataframe −series.str.extract(r'(\w+[a-z])(\d+)')ExampleLet’s see the below implementation to get a better understanding −import pandas as pd series = pd.Series(['abx123', 'bcd25', 'cxy30']) print("series is:", series) df ... Read More
Assume you have a series and the result for filtering armstrong numbers, original series is 0 153 1 323 2 371 3 420 4 500 dtype: int64 Armstrong numbers are:- 0 153 2 371 dtype: int64To solve this, we will follow the steps given below −Define a series.Create an empty list and set for loop to access all the series data.Set armstrong intial value is 0 and create temp variable to store series elements one by one. It is defined below, l = [] for val in data: armstrong = 0 ... Read More
Assume, you have a dataframe and the result for shuffling all the data in a series, The original series is 0 1 1 2 2 3 3 4 4 5 dtype: int64 The shuffled series is : 0 2 1 1 2 3 3 5 4 4 dtype: int64Solution 1Define a series.Apply random shuffle method takes series data as an argument and shuffles it.data = pd.Series([1, 2, 3, 4, 5]) print(data) rand.shuffle(data)ExampleLet’s see the below code to get a better understanding −import pandas as pd import random as rand data ... Read More
Assume, you have a dataframe, the result for converting float to int as, Before conversion Name object Age int64 Maths int64 Science int64 English int64 Result float64 dtype: object After conversion Name object Age int64 Maths int64 Science int64 English int64 Result int64 dtype: objectTo solve this, we will follow the steps given below −SolutionDefine a dataframeConvert float datatype column ‘Result’ into ‘int’ as follows −df.Result.astype(int)ExampleLet’s see the below implementation to get a better understanding −import pandas as pd data = {'Name': ['David', 'Adam', ... Read More
Assume you have dataframe and the result for swapping last two rows, Before swapping Name Age Maths Science English 0 David 13 98 75 79 1 Adam 12 59 96 45 2 Bob 12 66 55 70 3 Alex 13 95 49 60 4 Serina 12 70 78 80 After swapping Name Age Maths Science English 0 David 13 98 75 79 1 Adam 12 59 ... Read More