Why water is a huge issue for Texans right now ![]() “We never anticipated to have … the challenges.”Īlmost 300,000 Texas homes and businesses still were without power Thursday, down from around 4.5 million earlier in the week, according to utility tracker .īad weather has helped knock out power to a further 480,000 customers in many other states, including Oregon, Louisiana, Mississippi, Illinois, Kentucky, Ohio, West Virginia, Virginia and North Carolina, according to. We never thought it was going to be like this,” she said. “We have lived all over the States, being a military family. To add a little warmth, the family ran the burners on the stove. She said that during the times power has been off she and her husband have tried to keep their children’s minds off the cold by keeping them busy and bundled up. Texans face yet another day of misery as families struggle with unheated homes and water problems Brazziell/Austin American-Statesman/USA Today Network A winter storm that brought snow, ice, and plunging temperatures across Central Texas shut down roads and caused the electrical grid to shut down, leaving thousands of people without power. Jose' Nives tries to shovel his way out after getting stuck in the middle of the street. “And when we tried the few that are open, you have to stand in line for 20-30 minutes at a time, and then you just go in and get whatever is available, because stores are (largely) empty.” “We’re able to get enough to get by … but the grocery stores, most of them shut down,” Lemus told CNN’s Jim Sciutto on Thursday morning. In San Antonio, Claudia Lemus said power returned to her home Wednesday night – but many stores’ shelves were empty. “Texans deserve answers about why the shortfalls occurred, and how they’re going to be corrected and Texans will get those answers,” Abbott said. Greg Abbott was reassuring citizens he will get to the bottom of why so many people lost power this week as grid operators struggle to provide electricity. In Texas, communities are desperately seeking warmth and other necessities without electricity in freezing or near-freezing temperatures. Write a program to do the operation A*3+2 on matrix A.Woman fears for her daughter's health. Interview Question on Data Cleansing using Pythonġ. We have a function rename() to rename the columns.Įxample of renaming columns: print(data.rename(columns=)) We can remove the irrelevant data by using the del method.Įxample of removing irrelevant data: del data We can remove the repeated values by using the drop_duplicates() method.Įxample of removing repeated values: data.drop_duplicates() Using fillna() function, we can fill forward and fill backward as well.Įxample of replacing missing values by filling forward : data.fillna(method='pad')Įxample of replacing missing values by filling backward: data.fillna(method='backfill') We can use the replace() function or fillna() function to replace it with a constant value.Įxample of replacing missing values using replace(): from numpy import NaNĮxample of replacing missing values using fillna(): data.fillna(3) We have different options for replacing the missing values. We can find the missing values using isnull() function.Įxample of finding missing values: data.isnull()Įxample of removing missing values: data.dropna() Now let us see different operations we can use on the data frame. Now let us get the information about the data using the describe() and rank() functions.Įxample of describe() function: scribe() Let us first see the way to load the data frame.Įxample of loading CSV file as data frame: import pandas as pd When we are using pandas, we use the data frames. ![]() Creating a one dimensional numpy arrayĮxample of creating a one dimensional numpy array: import numpy as np There are many ways of creating numpy arrays using np.array() method. ![]() pip install numpyīefore learning about the operations we can perform using NumPy, let us look at the ways of creating NumPy arrays. We can use the below statements to install the modules. Installing required ModulesĪs said above we will be learning data cleansing using NumPy and Pandas modules. Besides this, there are a lot of applications where we need to handle the obtained information. For example, when one takes a data set one needs to remove null values, remove that part of data we need based on application, etc. What is Data Cleansing?ĭata Cleansing is the process of detecting and changing raw data by identifying incomplete, wrong, repeated, or irrelevant parts of the data. First, lets us see more on data cleaning. In this article, we will be learning to clean the data by using the Python modules NumPy and Pandas. We all know that the raw data we get needs to be cleansed to remove repeated values, missing values, etc. ![]() Here we are again with an article related to handling data, which plays an important role in all the domains.
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