Pandas Articles - Page 47 of 42

Input − Assume, you have a Series, 0    1 1    2 2    3 3    4 4    5Output −And, the result after replacing odd index with uppercase vowels as follows −0    1 1    A 2    3 3    U 4    5SolutionDefine a Series.Define uppercase alphabetsCreate lambda filter method and replace vowels in all index positions. It is defined belowvowels = re.findall(r'[AEIOU]', chars) result = pd.Series(filter(lambda x: r.choice(vowels) if(x%2!=0), l)data)Exampleimport pandas as pd import random as r l = [1, 2, 3, 4, 5] data = pd.Series(l) print(“Given series:”, data) vowels = list("AEIOU") ... Read More

Input − Assume, we have a Series, 0 2010-03-12 1 2011-3-1 2 2020-10-10 3 11-2-2Output − And, the result for valid dates in a series is, 0 2010-03-12 2 2020-10-10Solution 1Define a Series.Apply lambda filter method to validate a pattern in a series, data = pd.Series(l) result = pd.Series(filter(lambda x:re.match(r"\d{4}\W\d{2}\W\d{2}", x), data))Finally, check the result to the series using the isin() function.ExampleLet us see the following implementation to get a better understanding.import pandas as pd import re l = ['2010-03-12', '2011-3-1', '2020-10-10', '11-2-2'] data = pd.Series(l) for i, j in data.items():    if(re.match(r"\d{4}\W\d{2}\W\d{2}", j)):       print(i, j)Output0   ... Read More

IntroductionIn this post, I will show you how to perform Data Analysis with SQL style filtering with Pandas. Most of the corporate company’s data are stored in databases that require SQL to retrieve and manipulate it. For instance, there are companies like Oracle, IBM, Microsoft having their own databases with their own SQL implementations.Data scientists have to deal with SQL at some stage of their career as the data is not always stored in CSV files. I personally prefer to use Oracle, as the majority of my company’s data is stored in Oracle.Scenario – 1 Suppose we are given a ... Read More

IntroductionPandas have a dual selection capability to select the subset of data using the Index position or by using the Index labels. Inthis post, I will show you how to “Select a Subset Of Data Using Index Labels” using the index label.Remember, Python dictionaries and lists are built-in data structures that select their data either by using the index label or byindex position. A dictionary’s key must be a string, integer, or tuple while a List must either use integers (the position) or sliceobjects for selection.Pandas have .loc and.iloc attributes available to perform index operations in their own unique ways. ... Read More

In this post, we will go through the options handling large CSV files with Pandas.CSV files are common containers of data, If you have a large CSV file that you want to process with pandas effectively, you have a few options.Pandas is an in−memory toolYou need to be able to fit your data in memory to use pandas with it. If you can process portions of it at a time, you can read it into chunks and process each chunk. Alternatively, if you know that you should have enough memory to load the file, there are a few hints to ... Read More

Boolean indexing helps us to select the data from the DataFrames using a boolean vector. We need a DataFrame with a boolean index to use the boolean indexing. Let's see how to achieve the boolean indexing.Create a dictionary of data.Convert it into a DataFrame object with a boolean index as a vector.Now, access the data using boolean indexing.See the example below to get an idea.Exampleimport pandas as pd # data data = {    'Name': ['Hafeez', 'Srikanth', 'Rakesh'],    'Age': [19, 20, 19] } # creating a DataFrame with boolean index vector data_frame = pd.DataFrame(data, index = [True, False, True]) ... Read More

Pandas Data Frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. It can be created using python dict, list, and series etc. In this article, we will see how to add a new column to an existing data frame.So first let's create a data frame using pandas series. In the below example we are converting a pandas series to a Data Frame of one column, giving it a column name Month_no.Exampleimport pandas as pd s = pd.Series([6, 8, 3, 1, 12]) df = pd.DataFrame(s, columns=['Month_No']) print (df)OutputRunning the above code gives ... Read More

Pandas series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The elements of a pandas series can be accessed using various methods.Let's first create a pandas series and then access it's elements.Creating Pandas SeriesA panadas series is created by supplying data in various forms like ndarray, list, constants and the index values which must be unique and hashable. An example is given below.Exampleimport pandas as pd s = pd.Series([11, 8, 6, 14, 25], index = ['a', 'b', 'c', 'd', 'e']) print sOutputRunning the above code gives us the following result ... Read More