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Title | DataPrep——The easiest way to prepare data in |
Description | DataPrep is designed and optimized for computational notebooks, the most popular environment among data |
Keywords | N/A |
WebSite | dataprep.ai |
Host IP | 185.199.111.153 |
Location | - |
Site | Rank |
US$3,506,578
Last updated: 2022-06-18 11:01:12
dataprep.ai has Semrush global rank of 3,018,414. dataprep.ai has an estimated worth of US$ 3,506,578, based on its estimated Ads revenue. dataprep.ai receives approximately 404,606 unique visitors each day. Its web server is located in -, with IP address 185.199.111.153. According to SiteAdvisor, dataprep.ai is safe to visit. |
Purchase/Sale Value | US$3,506,578 |
Daily Ads Revenue | US$3,237 |
Monthly Ads Revenue | US$97,106 |
Yearly Ads Revenue | US$1,165,263 |
Daily Unique Visitors | 26,974 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
dataprep.ai. | A | 1800 | IP: 185.199.111.153 |
dataprep.ai. | A | 1800 | IP: 185.199.110.153 |
dataprep.ai. | A | 1800 | IP: 185.199.109.153 |
dataprep.ai. | A | 1800 | IP: 185.199.108.153 |
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dataprep.ai. | NS | 1800 | NS Record: dns1.registrar-servers.com. |
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dataprep.ai. | MX | 1800 | MX Record: 10 eforward3.registrar-servers.com. |
dataprep.ai. | MX | 1800 | MX Record: 20 eforward5.registrar-servers.com. |
dataprep.ai. | MX | 1800 | MX Record: 15 eforward4.registrar-servers.com. |
dataprep.ai. | TXT | 1800 | TXT Record: v=spf1 include:spf.efwd.registrar-servers.com ~all |
dataprep.ai. | TXT | 1799 | TXT Record: atlassian-domain-verification=ycGNI/DgomBkXMVf3Cif9YTSHkdtEnH5vfF/6IaVTKxhcDZuK8hWphig2GlRuenj |
Documentation Learning Team GitHub Brand DataPrep V0.4 is out now! Click here for more. ✕ The easiest way to prepare data in Python dataprep_eda.py from dataprep.datasets import load_dataset from dataprep.eda import create_report df = load_dataset( "titanic" ) create_report(df).show() dataprep_connector.py from dataprep.connector import connect dc = connect( "twitter" , _auth= {"client_id":client_id, "client_secret":client_secret} ) df = await dc.query( "twitter" , q= "covid-19" , _count= 1000 ) dataprep_clean.py from dataprep.datasets import load_dataset from dataprep.clean import clean_address df = load_dataset( "waste_hauler" ) clean_address(df, "LOCAL ADDRESS" ) Think different. "We were disappointed, if not surprised, to see that data wrangling still takes the lion’s share of time in a typical data professional’s day. Data preparation and cleansing takes valuable time away from real data science work and has a negative impact on overall job satisfaction." 2020 State of Data |
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