{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Soner Yıldırım",
  "title_listed": "Pandas Isn't Going Anywhere: Why It's Still My Go-To for Data Wrangling",
  "keywords": "",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/pandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling/",
  "publish_date": "2026-05-17",
  "corpus_stage": "",
  "notes": "",
  "id": "253e1d749505",
  "collected_at": "2026-10-02T09:30:36",
  "title": "Pandas Isn’t Going Anywhere: Why It’s Still My Go-To for Data Wrangling | Towards Data Science",
  "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Programming](https://towardsdatascience.com/category/programming/)\n# Pandas Isn’t Going Anywhere: Why It’s Still My Go-To for Data Wrangling\nBillions of rows might be the exception, but for everything else, Pandas is still a highly reliable tool.\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nMay 17, 20265 min read\n![](../images/000.jpeg)\nWhen I first started learning data science in 2020, Pandas was one of the most popular tools. Although new tools focus on improving Pandas' weaknesses in handling very large datasets, I still use Pandas for many data cleaning, processing, and analysis tasks. Yes, Pandas gives me a hard time when working with billions of rows, but it is definitely more than enough for working with anything below that.\nI see Pandas being used in not only for EDA or in notebooks but also in production systems.\nIn this article, I'll go over some data cleaning and processing operations to demonstrate how capable Pandas is.\nLet's start with the dataset, which contains stock keeping units (SKUs) and a search API responses for these SKUs.\npython\n\n```\nimport pandas as pd\n\nsearch_results = pd.read_csv(\"search_results.csv\")\n\nsearch_results.head()\n```\n\n![](../images/001.png)\nSearch result is a list of dictionaries and looks like this:\npython\n\n```\nsearch_results.loc[0, \"search_result\"]\n\n\"[{'my_id': 'HBCV00007F5Y2B', 'distance': 1.0, 'entity': {}}, \n{'my_id': 'HBCV00007UPQBM', 'distance': 1.0, 'entity': {}}, \n{'my_id': 'HBCV00008I29IH', 'distance': 1.0, 'entity': {}}, \n{'my_id': 'HBCV00006U3ZYB', 'distance': 0.8961254358291626, 'entity': {}}, \n{'my_id': 'HBCV0000AFA4H6', 'distance': 0.8702399730682373, 'entity': {}}, \n{'my_id': 'HBCV00009CDGD4', 'distance': 0.86175537109375, 'entity': {}}, \n{'my_id': 'HBCV000046336T', 'distance': 0.8594968318939209, 'entity': {}}, \n{'my_id': 'HBCV00009QDZRT', 'distance': 0.8572311997413635, 'entity': {}}, \n{'my_id': 'HBCV00008E11P3', 'distance': 0.8553324937820435, 'entity': {}}, \n{'my_id': 'HBV00000C4IY6', 'distance': 0.8539167642593384, 'entity': {}}] \n... and 5 entities remaining\"\n```\n\nAs we see in the output, it's not a proper list of dictionary format because of the last part (\"... and 5 entities remaining\"). Also, it's saved as a single string.\nIn order to make better use of it, we need to convert it to a proper list of dictionaries. The following line of code removes the last part by splitting the string at \"...\" and takes the first split. \npython\n\n```\nsearch_results.loc[0, \"search_result\"].split(\"...\")[0].strip()\n```\n\nHowever, the output is still a single string. We can use the built-in ast module of Python to convert it to a list:\npython\n\n```\nimport ast\n\nres = ast.literal_eval(search_results.loc[0, \"search_result\"].split(\"...\")[0].strip())\n\nres\n\n[{'my_id': 'HBCV00007F5Y2B', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00007UPQBM', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00008I29IH', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00006U3ZYB', 'distance': 0.8961254358291626, 'entity': {}},\n {'my_id': 'HBCV0000AFA4H6', 'distance': 0.8702399730682373, 'entity': {}},\n {'my_id': 'HBCV00009CDGD4', 'distance': 0.86175537109375, 'entity': {}},\n {'my_id': 'HBCV000046336T', 'distance': 0.8594968318939209, 'entity': {}},\n {'my_id': 'HBCV00009QDZRT', 'distance': 0.8572311997413635, 'entity': {}},\n {'my_id': 'HBCV00008E11P3', 'distance': 0.8553324937820435, 'entity': {}},\n {'my_id': 'HBV00000C4IY6', 'distance': 0.8539167642593384, 'entity': {}}]\n```\n\nWe now have the search results as a proper list of dictionaries. This was only for a single row. We need to apply the same operation to all SKUs (i.e. entire SKU column).\nOne option is to go over all the rows in a for loop and perform the same operation. However, this is not the best option. We should prefer vectorized operations when we can. A vectorized operation basically means executing the code on all rows at once.\nOn a single row, I used splitting to get rid of the last part of the string but it did not work in a vectorized operation. A more robust option seems to be using a regex.\npython\n\n```\nsearch_results.loc[:, 'search_result'] = search_results['search_result'].str.replace(r\"\\.\\.\\..*\", \"\", regex=True).str.strip()\n```\n\nThis code selects \"...\" and everything that comes after it and replaces them with nothing. In other words, it removes \"... and 5 entities remaining\" part.\nWe now have all the rows in the search results column as a proper list of dictionaries.\npython\n\n```\nsearch_results.loc[10, \"search_result\"]\n\n\"[{'my_id': 'HBCV00007F5Y2B', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00007UPQBM', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00008I29IH', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00006U3ZYB', 'distance': 0.8961254358291626, 'entity': {}},\n {'my_id': 'HBCV0000AFA4H6', 'distance': 0.8702399730682373, 'entity': {}},\n {'my_id': 'HBCV00009CDGD4', 'distance': 0.86175537109375, 'entity': {}},\n {'my_id': 'HBCV000046336T', 'distance': 0.8594968318939209, 'entity': {}},\n {'my_id': 'HBCV00009QDZRT', 'distance': 0.8572311997413635, 'entity': {}},\n {'my_id': 'HBCV00008E11P3', 'distance': 0.8553324937820435, 'entity': {}},\n {'my_id': 'HBV00000C4IY6', 'distance': 0.8539167642593384, 'entity': {}}]\"\n```\n\nThey're still saved as a string but I can easily convert them to a list using the ast module, which I will do in the next step.\nWhat I'm interested in is the SKUs returned in the search results. I'll create a new column by extracting the SKUs in the dictionaries. I can access them using the \"my_id\" key of the dictionary.\nThere are 3 parts of this operation:\n  * Convert the search result string to list using the literal_eval function\n  * Extract SKU from the my_id key of the dictionary\n  * Do this in a list comprehension to get SKUs from all the dictionaries in the list\n\n\nWe can do all these operations by applying a lambda function to all rows as follows:\npython\n\n```\nsearch_results.loc[:, \"result_skus\"] = \\\nsearch_results[\"search_result\"].apply(lambda x: [item['my_id'] for item in ast.literal_eval(x)])\n\nsearch_results.head()\n```\n\n![](../images/body-cf70021b266e55e509f0.png)\nEach row in the result_skus column contains a list of 10 SKUs. Let's say I need to have these 10 SKUs in different rows. For each row in the sku column, there will be 10 rows created from the list in the result_skus column. There is a very simple way of doing this in Pandas, which is the explode function.\npython\n\n```\ndata = search_results[[\"sku\", \"result_skus\"]].explode(\"result_skus\", ignore_index=True)\n\ndata.head()\n```\n\n![](../images/body-c7283090c0a91110ebe6.png)\nWe created a new dataframe with sku and result_skus column. The drawing below demonstrates what the explode function does:\n![](../images/body-0b5e33bd897feef34b63.png)\nConsider the opposite. We have a dataframe as shown above but want to have all results for an sku in a single row. \nWe can use the groupby function to group the rows by sku and then apply the list function on the result_skus column:\npython\n\n```\nnew_data = data.groupby(\"sku\", as_index=False)[\"result_skus\"].apply(list)\n\nnew_data.head()\n```\n\nThis will get us back to the previous step:\n![](../images/body-04420ae052d30bc8470b.png)\nUsing the explode function, we created a dataframe with a separate row for each sku in the result_skus column. What if we need to have them separated to different columns instead of rows?\nOne option is to apply the pd.Series function to the result_skus column and concatenate the resulting columns to the original dataframe.\npython\n\n```\nnew_cols = new_data[\"result_skus\"].apply(pd.Series)\n\nnew_data = pd.concat([new_data, new_cols], axis=1)\n\nnew_data.head()\n```\n\n![](../images/body-48d59644c6e8a92e7f95.png)\nColumns from 0 to 9 contains the 10 SKUs in the result_skus column. This code using the apply function is not a vectorized operation. \nWe have another option, which is vectorized and much faster.\npython\n\n```\nnew_cols = pd.DataFrame(new_data[\"result_skus\"].tolist())\n\nnew_data = pd.concat([new_data, new_cols], axis=1)\n```\n\nThis code will give us the same dataframe as above but much faster.\nI demonstrated a typical data cleaning and processing task a data scientist or analyst may encounter in their job. I've been in the field for over 5 years and Pandas has always been enough to do what I need except for when working very large datasets (e.g. billions of rows). \nThe tools that are better fit for such large datasets have similar syntax to Pandas. For example, PySpark is kind of a mixture of Pandas and SQL. Polars is very similar to Pandas in terms of syntax. Thus, learning and practicind Pandas is still a highly valuable skill for anyone working in the data science and AI domain.\nThank you for reading.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-11c1a04e02d8a14729da.png)\nWritten By\n## [Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[See all from Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[Data Analysis](https://towardsdatascience.com/tag/data-analysis/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Pandas](https://towardsdatascience.com/tag/pandas/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F&title=Pandas%20Isn%E2%80%99t%20Going%20Anywhere%3A%20Why%20It%E2%80%99s%20Still%20My%20Go-To%20for%20Data%20Wrangling)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F&title=Pandas%20Isn%E2%80%99t%20Going%20Anywhere%3A%20Why%20It%E2%80%99s%20Still%20My%20Go-To%20for%20Data%20Wrangling)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F&text=Pandas%20Isn%E2%80%99t%20Going%20Anywhere%3A%20Why%20It%E2%80%99s%20Still%20My%20Go-To%20for%20Data%20Wrangling)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](../images/002.png)](https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/)\n## [How To Perform Feature Selection for Regression Problems](https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nIn this article I explain what feature selection is and how to perform it before training a regression model in Python.\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nAugust 8, 20206 min read\n  * [![](../images/003.jpg)](https://towardsdatascience.com/pandas-combining-data-b190d793b626/)\n## [Pandas: Combining Data](https://towardsdatascience.com/pandas-combining-data-b190d793b626/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nPyTrix #5: More Data Manipulation with Pandas\n[Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\nJuly 13, 20207 min read\n  * [![](../images/004.png)](https://towardsdatascience.com/utilization-of-twitters-api-with-r-advanced-techniques-3985b43df86d/)\n## [Utilization of Twitter's API with R - Advanced Techniques.](https://towardsdatascience.com/utilization-of-twitters-api-with-r-advanced-techniques-3985b43df86d/)\n[Programming](https://towardsdatascience.com/category/programming/)\nThe maddening adventures of extracting millions of tweets.\n[Justin Cocco](https://towardsdatascience.com/author/justincocco/)\nNovember 21, 202025 min read\n  * [![](../images/005.png)](https://towardsdatascience.com/an-introduction-to-nine-essential-machine-learning-algorithms-ee0efbb61e0/)\n## [An Introduction to Nine Essential Machine Learning Algorithms](https://towardsdatascience.com/an-introduction-to-nine-essential-machine-learning-algorithms-ee0efbb61e0/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nIntuitive explanations of the most popular machine learning models.\n[Terence Shin, MSc, MBA](https://towardsdatascience.com/author/terenceshin/)\nApril 2, 20207 min read\n  * [![](../images/006.jpeg)](https://towardsdatascience.com/an-intuitive-guide-to-auto-encoders-theory-code-and-visualization-3cc2a6a30d2c/)\n## [An Intuitive Guide to Auto-Encoders: Theory, Code and Visualization](https://towardsdatascience.com/an-intuitive-guide-to-auto-encoders-theory-code-and-visualization-3cc2a6a30d2c/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nUsing Auto-Encoders to gain insight into data\n[Victor Sim](https://towardsdatascience.com/author/victorsim14/)\nSeptember 10, 20205 min read\n  * [![](../images/007.jpeg)](https://towardsdatascience.com/topic-modeling-and-sentiment-analysis-on-amazon-alexa-reviews-part-ii-47ff96541d19/)\n## [Topic Modeling and Sentiment Analysis on Amazon Alexa Reviews - Part II](https://towardsdatascience.com/topic-modeling-and-sentiment-analysis-on-amazon-alexa-reviews-part-ii-47ff96541d19/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nFollow up to \"Topic Modeling and Sentiment Analysis on Amazon Alexa Reviews\" analyzing and comparing different Echo models.\n[Muriel Kosaka](https://towardsdatascience.com/author/murielsayaka/)\nDecember 15, 20206 min read\n  * [![](../images/008.jpg)](https://towardsdatascience.com/okay-youve-trained-the-best-machine-learning-model-what-s-next-e7b8f167006e/)\n## [Okay, You've Trained the Best Machine Learning Model. What's Next?](https://towardsdatascience.com/okay-youve-trained-the-best-machine-learning-model-what-s-next-e7b8f167006e/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nAn MLOps project beyond modeling in Jupyter Notebook\n[Albers Uzila](https://towardsdatascience.com/author/dwiuzila/)\nJune 4, 20233 min read\n  * [![](../images/009.jpg)](https://towardsdatascience.com/reducing-memory-usage-in-pandas-with-smaller-datatypes-b527635830af/)\n## [Reducing memory usage in pandas with smaller datatypes](https://towardsdatascience.com/reducing-memory-usage-in-pandas-with-smaller-datatypes-b527635830af/)\n[Programming](https://towardsdatascience.com/category/programming/)\nOptimising pandas memory useage by the effective use of datatypes\n[Parul Pandey](https://towardsdatascience.com/author/pandeyparul/)\nMarch 15, 20210 min read\n  * [![](../images/010.jpg)](https://towardsdatascience.com/create-powerful-model-explanations-for-classification-problems-with-logistic-regression-9bdd5f1c3648/)\n## [Create Powerful Model Explanations for Classification Problems with Logistic Regression](https://towardsdatascience.com/create-powerful-model-explanations-for-classification-problems-with-logistic-regression-9bdd5f1c3648/)\n[Programming](https://towardsdatascience.com/category/programming/)\nA practitioner's guide, with a demonstration using the IBM Telco Churn dataset\n[Jin Cui](https://towardsdatascience.com/author/jin-cui/)\nDecember 30, 20229 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ \"Productivity Tips, Data Career Insights, and Other Recent Must-Reads\")\n\n\n",
  "metadata": {
    "title": "Pandas Isn’t Going Anywhere: Why It’s Still My Go-To for Data Wrangling | Towards Data Science",
    "description": "Billions of rows might be the exception, but for everything else, Pandas is still a highly reliable tool.",
    "keywords": null,
    "author": "Soner Yıldırım",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "Pandas Isn’t Going Anywhere: Why It’s Still My Go-To for Data Wrangling",
    "og:description": "Billions of rows might be the exception, but for everything else, Pandas is still a highly reliable tool.",
    "og:url": "https://towardsdatascience.com/pandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/efe-yagiz-soysal-SgU7-iZn8m8-unsplash-Medium.jpeg",
    "og:image:width": "640",
    "og:image:height": "427",
    "og:image:type": "image/jpeg",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "Soner Yıldırım",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "5 min read",
    "article:published_time": "2026-05-17T09:00:08.000Z",
    "article:modified_time": "2026-05-17T09:00:08.000Z",
    "article:author": "https://towardsdatascience.com/author/sonery/"
  },
  "paywall": {
    "listed_paid": false,
    "detected": false,
    "marker": "",
    "public_only": false,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 202,
    "error": ""
  },
  "code_blocks": [
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_000.txt",
      "chars": 95
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_001.txt",
      "chars": 784
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_002.txt",
      "chars": 63
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_003.txt",
      "chars": 817
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_004.txt",
      "chars": 126
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_005.txt",
      "chars": 755
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_006.txt",
      "chars": 161
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_007.txt",
      "chars": 101
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_008.txt",
      "chars": 91
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_009.txt",
      "chars": 121
    },
    {
      "language": "txt",
      "file": "attachments/253e1d749505/code/block_010.txt",
      "chars": 110
    }
  ],
  "images": [
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:50:39Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:50:39Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:50:40Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:50:40Z"
    }
  ],
  "audio": [],
  "videos": [
    {
      "url": "https://www.youtube.com/c/TowardsDataScience",
      "kind": "channel_reference",
      "status": "reference_only"
    }
  ],
  "links": [
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/latest/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/editors-pick/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/the-variable/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/shipai/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/submissions/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/programming/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/sonery/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/data-analysis/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/pandas/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/python/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/seralouk/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/pandas-combining-data-b190d793b626/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/kurtispykes/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/utilization-of-twitters-api-with-r-advanced-techniques-3985b43df86d/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/justincocco/"
    },
    {
      "type": "external",
      "href": "https://write.insightmediagroup.io"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/company/towards-data-science/"
    },
    {
      "type": "external",
      "href": "https://x.com/TDataScience"
    },
    {
      "type": "external",
      "href": "https://www.facebook.com/sharer/sharer.php?title=Pandas+Isn%E2%80%99t+Going+Anywhere%3A+Why+It%E2%80%99s+Still+My+Go-To+for+Data+Wrangling&u=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/shareArticle?mini=true&title=Pandas+Isn%E2%80%99t+Going+Anywhere%3A+Why+It%E2%80%99s+Still+My+Go-To+for+Data+Wrangling&url=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F"
    },
    {
      "type": "external",
      "href": "https://x.com/share?text=Pandas+Isn%E2%80%99t+Going+Anywhere%3A+Why+It%E2%80%99s+Still+My+Go-To+for+Data+Wrangling&url=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F"
    },
    {
      "type": "external",
      "href": "https://www.youtube.com/c/TowardsDataScience"
    },
    {
      "type": "external",
      "href": "https://www.threads.net/@towardsdatascience"
    },
    {
      "type": "external",
      "href": "https://bsky.app/profile/towardsdatascience.com"
    },
    {
      "type": "external",
      "href": "https://thenewstack.io"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh"
    },
    {
      "type": "external",
      "href": "https://insightmediagroup.io/creator-network"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-20T17:27:04Z",
  "source_url": "https://towardsdatascience.com/pandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling/",
  "delivery_status": "unverified",
  "discovery_status": "http_403",
  "missing_reason": "源页面未重新核验:http_403；已按保留正文补采图片",
  "repair_error": "",
  "actual_video_files": [],
  "expected_saved_body_images": 4,
  "image_discovery": "saved_markdown",
  "source_body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Programming](https://towardsdatascience.com/category/programming/)\n# Pandas Isn’t Going Anywhere: Why It’s Still My Go-To for Data Wrangling\nBillions of rows might be the exception, but for everything else, Pandas is still a highly reliable tool.\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nMay 17, 20265 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/efe-yagiz-soysal-SgU7-iZn8m8-unsplash-Medium.jpeg)\nWhen I first started learning data science in 2020, Pandas was one of the most popular tools. Although new tools focus on improving Pandas' weaknesses in handling very large datasets, I still use Pandas for many data cleaning, processing, and analysis tasks. Yes, Pandas gives me a hard time when working with billions of rows, but it is definitely more than enough for working with anything below that.\nI see Pandas being used in not only for EDA or in notebooks but also in production systems.\nIn this article, I'll go over some data cleaning and processing operations to demonstrate how capable Pandas is.\nLet's start with the dataset, which contains stock keeping units (SKUs) and a search API responses for these SKUs.\npython\n\n```\nimport pandas as pd\n\nsearch_results = pd.read_csv(\"search_results.csv\")\n\nsearch_results.head()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/image-176.png)\nSearch result is a list of dictionaries and looks like this:\npython\n\n```\nsearch_results.loc[0, \"search_result\"]\n\n\"[{'my_id': 'HBCV00007F5Y2B', 'distance': 1.0, 'entity': {}}, \n{'my_id': 'HBCV00007UPQBM', 'distance': 1.0, 'entity': {}}, \n{'my_id': 'HBCV00008I29IH', 'distance': 1.0, 'entity': {}}, \n{'my_id': 'HBCV00006U3ZYB', 'distance': 0.8961254358291626, 'entity': {}}, \n{'my_id': 'HBCV0000AFA4H6', 'distance': 0.8702399730682373, 'entity': {}}, \n{'my_id': 'HBCV00009CDGD4', 'distance': 0.86175537109375, 'entity': {}}, \n{'my_id': 'HBCV000046336T', 'distance': 0.8594968318939209, 'entity': {}}, \n{'my_id': 'HBCV00009QDZRT', 'distance': 0.8572311997413635, 'entity': {}}, \n{'my_id': 'HBCV00008E11P3', 'distance': 0.8553324937820435, 'entity': {}}, \n{'my_id': 'HBV00000C4IY6', 'distance': 0.8539167642593384, 'entity': {}}] \n... and 5 entities remaining\"\n```\n\nAs we see in the output, it's not a proper list of dictionary format because of the last part (\"... and 5 entities remaining\"). Also, it's saved as a single string.\nIn order to make better use of it, we need to convert it to a proper list of dictionaries. The following line of code removes the last part by splitting the string at \"...\" and takes the first split. \npython\n\n```\nsearch_results.loc[0, \"search_result\"].split(\"...\")[0].strip()\n```\n\nHowever, the output is still a single string. We can use the built-in ast module of Python to convert it to a list:\npython\n\n```\nimport ast\n\nres = ast.literal_eval(search_results.loc[0, \"search_result\"].split(\"...\")[0].strip())\n\nres\n\n[{'my_id': 'HBCV00007F5Y2B', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00007UPQBM', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00008I29IH', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00006U3ZYB', 'distance': 0.8961254358291626, 'entity': {}},\n {'my_id': 'HBCV0000AFA4H6', 'distance': 0.8702399730682373, 'entity': {}},\n {'my_id': 'HBCV00009CDGD4', 'distance': 0.86175537109375, 'entity': {}},\n {'my_id': 'HBCV000046336T', 'distance': 0.8594968318939209, 'entity': {}},\n {'my_id': 'HBCV00009QDZRT', 'distance': 0.8572311997413635, 'entity': {}},\n {'my_id': 'HBCV00008E11P3', 'distance': 0.8553324937820435, 'entity': {}},\n {'my_id': 'HBV00000C4IY6', 'distance': 0.8539167642593384, 'entity': {}}]\n```\n\nWe now have the search results as a proper list of dictionaries. This was only for a single row. We need to apply the same operation to all SKUs (i.e. entire SKU column).\nOne option is to go over all the rows in a for loop and perform the same operation. However, this is not the best option. We should prefer vectorized operations when we can. A vectorized operation basically means executing the code on all rows at once.\nOn a single row, I used splitting to get rid of the last part of the string but it did not work in a vectorized operation. A more robust option seems to be using a regex.\npython\n\n```\nsearch_results.loc[:, 'search_result'] = search_results['search_result'].str.replace(r\"\\.\\.\\..*\", \"\", regex=True).str.strip()\n```\n\nThis code selects \"...\" and everything that comes after it and replaces them with nothing. In other words, it removes \"... and 5 entities remaining\" part.\nWe now have all the rows in the search results column as a proper list of dictionaries.\npython\n\n```\nsearch_results.loc[10, \"search_result\"]\n\n\"[{'my_id': 'HBCV00007F5Y2B', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00007UPQBM', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00008I29IH', 'distance': 1.0, 'entity': {}},\n {'my_id': 'HBCV00006U3ZYB', 'distance': 0.8961254358291626, 'entity': {}},\n {'my_id': 'HBCV0000AFA4H6', 'distance': 0.8702399730682373, 'entity': {}},\n {'my_id': 'HBCV00009CDGD4', 'distance': 0.86175537109375, 'entity': {}},\n {'my_id': 'HBCV000046336T', 'distance': 0.8594968318939209, 'entity': {}},\n {'my_id': 'HBCV00009QDZRT', 'distance': 0.8572311997413635, 'entity': {}},\n {'my_id': 'HBCV00008E11P3', 'distance': 0.8553324937820435, 'entity': {}},\n {'my_id': 'HBV00000C4IY6', 'distance': 0.8539167642593384, 'entity': {}}]\"\n```\n\nThey're still saved as a string but I can easily convert them to a list using the ast module, which I will do in the next step.\nWhat I'm interested in is the SKUs returned in the search results. I'll create a new column by extracting the SKUs in the dictionaries. I can access them using the \"my_id\" key of the dictionary.\nThere are 3 parts of this operation:\n  * Convert the search result string to list using the literal_eval function\n  * Extract SKU from the my_id key of the dictionary\n  * Do this in a list comprehension to get SKUs from all the dictionaries in the list\n\n\nWe can do all these operations by applying a lambda function to all rows as follows:\npython\n\n```\nsearch_results.loc[:, \"result_skus\"] = \\\nsearch_results[\"search_result\"].apply(lambda x: [item['my_id'] for item in ast.literal_eval(x)])\n\nsearch_results.head()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/image-177-1024x174.png)\nEach row in the result_skus column contains a list of 10 SKUs. Let's say I need to have these 10 SKUs in different rows. For each row in the sku column, there will be 10 rows created from the list in the result_skus column. There is a very simple way of doing this in Pandas, which is the explode function.\npython\n\n```\ndata = search_results[[\"sku\", \"result_skus\"]].explode(\"result_skus\", ignore_index=True)\n\ndata.head()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/image-184.png)\nWe created a new dataframe with sku and result_skus column. The drawing below demonstrates what the explode function does:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/image-185-1024x225.png)\nConsider the opposite. We have a dataframe as shown above but want to have all results for an sku in a single row. \nWe can use the groupby function to group the rows by sku and then apply the list function on the result_skus column:\npython\n\n```\nnew_data = data.groupby(\"sku\", as_index=False)[\"result_skus\"].apply(list)\n\nnew_data.head()\n```\n\nThis will get us back to the previous step:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/image-187-1024x260.png)\nUsing the explode function, we created a dataframe with a separate row for each sku in the result_skus column. What if we need to have them separated to different columns instead of rows?\nOne option is to apply the pd.Series function to the result_skus column and concatenate the resulting columns to the original dataframe.\npython\n\n```\nnew_cols = new_data[\"result_skus\"].apply(pd.Series)\n\nnew_data = pd.concat([new_data, new_cols], axis=1)\n\nnew_data.head()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/image-189-1024x283.png)\nColumns from 0 to 9 contains the 10 SKUs in the result_skus column. This code using the apply function is not a vectorized operation. \nWe have another option, which is vectorized and much faster.\npython\n\n```\nnew_cols = pd.DataFrame(new_data[\"result_skus\"].tolist())\n\nnew_data = pd.concat([new_data, new_cols], axis=1)\n```\n\nThis code will give us the same dataframe as above but much faster.\nI demonstrated a typical data cleaning and processing task a data scientist or analyst may encounter in their job. I've been in the field for over 5 years and Pandas has always been enough to do what I need except for when working very large datasets (e.g. billions of rows). \nThe tools that are better fit for such large datasets have similar syntax to Pandas. For example, PySpark is kind of a mixture of Pandas and SQL. Polars is very similar to Pandas in terms of syntax. Thus, learning and practicind Pandas is still a highly valuable skill for anyone working in the data science and AI domain.\nThank you for reading.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657090323_hn276a.png)\nWritten By\n## [Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[See all from Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[Data Analysis](https://towardsdatascience.com/tag/data-analysis/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Pandas](https://towardsdatascience.com/tag/pandas/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F&title=Pandas%20Isn%E2%80%99t%20Going%20Anywhere%3A%20Why%20It%E2%80%99s%20Still%20My%20Go-To%20for%20Data%20Wrangling)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F&title=Pandas%20Isn%E2%80%99t%20Going%20Anywhere%3A%20Why%20It%E2%80%99s%20Still%20My%20Go-To%20for%20Data%20Wrangling)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fpandas-isnt-going-anywhere-why-its-still-my-go-to-for-data-wrangling%2F&text=Pandas%20Isn%E2%80%99t%20Going%20Anywhere%3A%20Why%20It%E2%80%99s%20Still%20My%20Go-To%20for%20Data%20Wrangling)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/08/1XgGa0BE3mTXBHdzGHHp62w.png)](https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/)\n## [How To Perform Feature Selection for Regression Problems](https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nIn this article I explain what feature selection is and how to perform it before training a regression model in Python.\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nAugust 8, 20206 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/0F_ins9ztKrmJ2mgH-scaled.jpg)](https://towardsdatascience.com/pandas-combining-data-b190d793b626/)\n## [Pandas: Combining Data](https://towardsdatascience.com/pandas-combining-data-b190d793b626/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nPyTrix #5: More Data Manipulation with Pandas\n[Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\nJuly 13, 20207 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/11/1ZQxwh_Z6vC42kJ5iwEuyYg.png)](https://towardsdatascience.com/utilization-of-twitters-api-with-r-advanced-techniques-3985b43df86d/)\n## [Utilization of Twitter's API with R - Advanced Techniques.](https://towardsdatascience.com/utilization-of-twitters-api-with-r-advanced-techniques-3985b43df86d/)\n[Programming](https://towardsdatascience.com/category/programming/)\nThe maddening adventures of extracting millions of tweets.\n[Justin Cocco](https://towardsdatascience.com/author/justincocco/)\nNovember 21, 202025 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/04/1W5ia8bYCS3ZW3LXxU7wSTQ.png)](https://towardsdatascience.com/an-introduction-to-nine-essential-machine-learning-algorithms-ee0efbb61e0/)\n## [An Introduction to Nine Essential Machine Learning Algorithms](https://towardsdatascience.com/an-introduction-to-nine-essential-machine-learning-algorithms-ee0efbb61e0/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nIntuitive explanations of the most popular machine learning models.\n[Terence Shin, MSc, MBA](https://towardsdatascience.com/author/terenceshin/)\nApril 2, 20207 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1A-Foet4BRO2DJl5R-PCF-w-scaled.jpeg)](https://towardsdatascience.com/an-intuitive-guide-to-auto-encoders-theory-code-and-visualization-3cc2a6a30d2c/)\n## [An Intuitive Guide to Auto-Encoders: Theory, Code and Visualization](https://towardsdatascience.com/an-intuitive-guide-to-auto-encoders-theory-code-and-visualization-3cc2a6a30d2c/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nUsing Auto-Encoders to gain insight into data\n[Victor Sim](https://towardsdatascience.com/author/victorsim14/)\nSeptember 10, 20205 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/02/1pCvAG8BFYn775Kug1-HdUg-scaled.jpeg)](https://towardsdatascience.com/topic-modeling-and-sentiment-analysis-on-amazon-alexa-reviews-part-ii-47ff96541d19/)\n## [Topic Modeling and Sentiment Analysis on Amazon Alexa Reviews - Part II](https://towardsdatascience.com/topic-modeling-and-sentiment-analysis-on-amazon-alexa-reviews-part-ii-47ff96541d19/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nFollow up to \"Topic Modeling and Sentiment Analysis on Amazon Alexa Reviews\" analyzing and comparing different Echo models.\n[Muriel Kosaka](https://towardsdatascience.com/author/murielsayaka/)\nDecember 15, 20206 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/06/0sXRuspejLu4BbBo1-scaled.jpg)](https://towardsdatascience.com/okay-youve-trained-the-best-machine-learning-model-what-s-next-e7b8f167006e/)\n## [Okay, You've Trained the Best Machine Learning Model. What's Next?](https://towardsdatascience.com/okay-youve-trained-the-best-machine-learning-model-what-s-next-e7b8f167006e/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nAn MLOps project beyond modeling in Jupyter Notebook\n[Albers Uzila](https://towardsdatascience.com/author/dwiuzila/)\nJune 4, 20233 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/03/0Ti9hDWaZH8UphPdM-scaled.jpg)](https://towardsdatascience.com/reducing-memory-usage-in-pandas-with-smaller-datatypes-b527635830af/)\n## [Reducing memory usage in pandas with smaller datatypes](https://towardsdatascience.com/reducing-memory-usage-in-pandas-with-smaller-datatypes-b527635830af/)\n[Programming](https://towardsdatascience.com/category/programming/)\nOptimising pandas memory useage by the effective use of datatypes\n[Parul Pandey](https://towardsdatascience.com/author/pandeyparul/)\nMarch 15, 20210 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/035MLTcbCDgzxMAC4-scaled.jpg)](https://towardsdatascience.com/create-powerful-model-explanations-for-classification-problems-with-logistic-regression-9bdd5f1c3648/)\n## [Create Powerful Model Explanations for Classification Problems with Logistic Regression](https://towardsdatascience.com/create-powerful-model-explanations-for-classification-problems-with-logistic-regression-9bdd5f1c3648/)\n[Programming](https://towardsdatascience.com/category/programming/)\nA practitioner's guide, with a demonstration using the IBM Telco Churn dataset\n[Jin Cui](https://towardsdatascience.com/author/jin-cui/)\nDecember 30, 20229 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ \"Productivity Tips, Data Career Insights, and Other Recent Must-Reads\")\n\n\n",
  "body_base_path": "attachments/253e1d749505/pages/",
  "body_markdown_path": "attachments/253e1d749505/pages/archived-body.md",
  "local_html": "attachments/253e1d749505/pages/archived-body.html",
  "body_source": "prior_capture"
}