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

Assume, you have a dataframe, DataFrame is:  id mark age 0 1 70   12 1 2 60   13 2 3 40   12 3 4 50   13 4 5 80   12 5 6 90   13 6 7 60   12And, the result for selecting any random odd index row is, Random odd index row is:  id    4 mark   50 age    13SolutionTo solve this, we will follow the steps given below −Define a dataframeCreate an empty list to append odd index valuesCreate a for loop to access all the index. It is defined ... Read More

Assume, you have two dataframe, first dataframe is  id country 0 1 India 1 2 UK 2 3 US 3 4 China second dataframe is  id City 0 1 Chennai 1 11 Cambridge 2 22 Chicago 3 4 ChengduAnd the result for merging based on same column is, Merging data based on same column - id  id    country    City 0 1    India    Chennai 1 4    China    ChengduSolutionTo solve this, we will follow the steps given below −Define a two dataframesMerge two dataframes based on the same column id is defined below, pd.merge(first_df, ... Read More

Assume, you have a dataframe,  col1 col2 0 o    e 1 e    e 2 i    u 3 e    o 4 i    i 5 u    o 6 e    a 7 u    o 8 a    u 9 e    aThe result for matched index and count is, index is  col1 col2 1 e    e 4 i    i count is 2SolutionTo solve this, we will follow the steps given below −Define a dataframeCompare first and second matching index values using the below method, df.iloc[np.where(df.col1==df.col2)])Find the total count of matched columns using the ... Read More