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A regular expression is a sequence of characters that define a search pattern. In this program, we will use these regular expressions to filter valid and invalid emails in a Pandas series. We will define a Pandas series with different emails and check which email is valid using Python's re library for regex operations. Email Validation Regex Pattern The regex pattern for email validation contains several components ? ^: Anchor for the start of the string [a-z0-9]: Character class to match lowercase letters and digits [\._]?: Optional dot or underscore character @: Required @ symbol ... Read More
In this program, we will convert date strings like "24 August 2020" into datetime format using Pandas. The to_datetime() function automatically parses various date string formats and converts them to standardized datetime objects. Algorithm Step 1: Define a Pandas series containing date strings. Step 2: Convert these date strings into datetime format using to_datetime(). Step 3: Print the results. Example Let's convert different date string formats to datetime ? import pandas as pd series = pd.Series(["24 August 2020", "25 December 2020 20:05"]) print("Original Series:") print(series) datetime_series = pd.to_datetime(series) print("DateTime Format:") ... Read More
Finding multiples of a number in a list is a common task in data analysis. NumPy provides efficient methods to identify multiples using vectorized operations and built-in functions like argwhere() and modulo operations. Using Basic Loop Method The traditional approach uses a loop to check each element ? import numpy as np listnum = np.arange(1, 20) multiples = [] n = 5 print("NumList:", listnum) for num in listnum: if num % n == 0: multiples.append(num) ... Read More
In this program, we will calculate the frequency of each element in a Pandas series. The function value_counts() in the pandas library helps us to find the frequency of elements. Algorithm Step 1: Define a Pandas series. Step 2: Print the frequency of each item using the value_counts() function. Example Code import pandas as pd series = pd.Series([10, 10, 20, 30, 40, 30, 50, 10, 60, 50, 50]) print("Series:", series) frequency = series.value_counts() print("Frequency of elements:", frequency) Output Series: 0 10 1 ... Read More
A percentile is a statistical measure that indicates the value below which a certain percentage of observations fall. In Pandas, you can calculate the nth percentile of a series using the quantile() method. Syntax series.quantile(q) Parameters: q − The quantile value between 0 and 1 (percentile/100) Example Here's how to calculate the 50th percentile (median) of a Pandas series ? import pandas as pd # Create a Pandas series series = pd.Series([10, 20, 30, 40, 50]) print("Series:") print(series) # Calculate 50th percentile percentile_50 = series.quantile(0.5) print(f"The 50th ... Read More
Subplots allow you to display multiple plots in a single figure by dividing it into a grid. When working with subplots, you can customize the x-axis ticks for each subplot independently using plt.xticks(). Understanding Subplot Layout The plt.subplot() function creates subplots using three parameters: nrows, ncols, and index. For example, plt.subplot(121) creates a 1×2 grid and selects the first subplot. Basic Subplot with Different X-ticks Here's how to create two subplots with custom x-tick positions − import matplotlib.pyplot as plt line1 = [21, 14, 81] line2 = [31, 6, 12] # First ... Read More
In this article, we will compare two Pandas series and print the differences between them. By difference, we mean the index positions where elements did not match, along with the actual values from both series. What is Series Comparison? Pandas provides the compare() method to identify differences between two series. This method returns a DataFrame showing only the positions where values differ, with columns representing each series. Basic Example Let's start with a simple comparison between two series ? import pandas as pd s1 = pd.Series([10, 20, 30, 40, 50, 60]) s2 = ... Read More
In this program, we will find the standard deviation of a Pandas series. Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. Syntax Series.std(axis=None, skipna=True, level=None, ddof=1, numeric_only=None) Parameters The std() method accepts several parameters: ddof − Delta Degrees of Freedom (default is 1) skipna − Exclude NaN values (default is True) axis − Not applicable for Series Example Let's calculate the standard deviation of a Pandas series using the std() function: ... Read More
The mean() function in the Pandas library can be used to find the arithmetic mean (average) of a series. This function calculates the sum of all values divided by the number of values. Syntax Series.mean(axis=None, skipna=True, level=None, numeric_only=None) Parameters The key parameters are: skipna: If True (default), excludes NaN values from calculation numeric_only: Include only numeric columns Example Here's how to calculate the mean of a Pandas series ? import pandas as pd series = pd.Series([10, 20, 30, 40, 50]) print("Pandas Series:") print(series) series_mean = ... Read More
In Pandas, you can append elements to a Series using the append() method or the newer concat() function. The append() method allows you to combine two Series, but note that it's deprecated in newer Pandas versions in favor of concat(). Using append() Method The traditional approach uses the append() method to combine Series ? import pandas as pd s1 = pd.Series([10, 20, 30, 40, 50]) s2 = pd.Series([11, 22, 33, 44, 55]) print("S1:") print(s1) print("S2:") print(s2) appended_series = s1.append(s2) print("Final Series after appending:") print(appended_series) S1: 0 10 ... Read More