Found 507 Articles for Pandas

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

Assume, you have a dataframe,  one  two three 0 1    2    3 1 4    5    6And the result for removing single column is,  two three 0 2    3 1 5    6The result for removing after more than one column is,  three 0 3 1 6To solve this, we will follow the steps given below −Solution 1Define a dataframeDelete a particular column using below method, del df['one']ExampleLet’s see the below code to get a better understanding −import pandas as pd data = [[1, 2, 3], [4, 5, 6]] df = pd.DataFrame(data, columns=('one', 'two', 'three')) print("Before ... Read More

Assume, you have the following series, Series is: 0    1 1    22 2    3 3    4 4    22 5    5 6    22And the result for the most repeated element is, Repeated element is: 22SolutionTo solve this, we will follow the below approach, Define a seriesSet initial count is 0 and max_count value as series first element value data[0]count = 0 max_count = data[0]Create for loop to access series data and set frequency_count as l.count(i)for i in data:    frequency_count = l.count(i)Set if condition to compare with max_count value, if the condition is true ... Read More