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hf-doc-build/doc-dev / datasets /pr_7865 /en /_app /immutable /nodes /45.4ef9bd4e.js

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	import{s as Gs,n as Ys,o as Is}from"../chunks/scheduler.d75c11ed.js";import{S as Xs,i as Fs,e as p,s as l,c as o,h as Ns,a as i,d as a,b as n,f as ks,g as d,j as r,k as Zs,l as Hs,m as e,n as m,t as f,o as c,p as h}from"../chunks/index.4ec9dfe9.js";import{C as zs,H as ot,E as Ls}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.17700746.js";import{C as u}from"../chunks/CodeBlock.a8529232.js";function Qs(is){let g,ft,dt,ct,M,ht,y,ut,j,rs="A tabular dataset is a generic dataset used to describe any data stored in rows and columns, where the rows represent an example and the columns represent a feature (can be continuous or categorical). These datasets are commonly stored in CSV files, Pandas DataFrames, and in database tables. This guide will show you how to load and create a tabular dataset from:",gt,$,os="<li>CSV files</li> <li>Pandas DataFrames</li> <li>HDF5 files</li> <li>Databases</li>",Mt,w,yt,J,ds='🤗 Datasets can read CSV files by specifying the generic <code>csv</code> dataset builder name in the <a href="/docs/datasets/pr_7865/en/package_reference/loading_methods#datasets.load_dataset">load_dataset()</a> method. To load more than one CSV file, pass them as a list to the <code>data_files</code> parameter:',jt,T,$t,b,ms="You can also map specific CSV files to the train and test splits:",wt,_,Jt,x,fs="To load remote CSV files, pass the URLs instead:",Tt,C,bt,v,cs="To load zipped CSV files:",_t,R,xt,U,Ct,q,hs='🤗 Datasets also supports loading datasets from <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html" rel="nofollow">Pandas DataFrames</a> with the <a href="/docs/datasets/pr_7865/en/package_reference/main_classes#datasets.Dataset.from_pandas">from_pandas()</a> method:',vt,k,Rt,Z,us="Use the <code>splits</code> parameter to specify the name of the dataset split:",Ut,G,qt,Y,gs='If the dataset doesn’t look as expected, you should explicitly <a href="loading#specify-features">specify your dataset features</a>. A <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.html" rel="nofollow">pandas.Series</a> may not always carry enough information for Arrow to automatically infer a data type. For example, if a DataFrame is of length <code>0</code> or if the Series only contains <code>None/NaN</code> objects, the type is set to <code>null</code>.',kt,I,Zt,X,Ms='<a href="https://www.hdfgroup.org/solutions/hdf5/" rel="nofollow">HDF5</a> files are commonly used for storing large amounts of numerical data in scientific computing and machine learning. Loading HDF5 files with 🤗 Datasets is similar to loading CSV files:',Gt,F,Yt,N,ys="Note that the HDF5 loader assumes that the file has “tabular” structure, i.e. that all datasets in the file have (the same number of) rows on their first dimension.",It,H,Xt,z,js="Datasets stored in databases are typically accessed with SQL queries. With 🤗 Datasets, you can connect to a database, query for the data you need, and create a dataset out of it. Then you can use all the processing features of 🤗 Datasets to prepare your dataset for training.",Ft,L,Nt,Q,$s="SQLite is a small, lightweight database that is fast and easy to set up. You can use an existing database if you’d like, or follow along and start from scratch.",Ht,D,ws='Start by creating a quick SQLite database with this <a href="https://github.com/nytimes/covid-19-data/blob/master/us-states.csv" rel="nofollow">Covid-19 data</a> from the New York Times:',zt,S,Lt,V,Js="This creates a <code>states</code> table in the <code>us_covid_data.db</code> database which you can now load into a dataset.",Qt,E,Ts='To connect to the database, you’ll need the <a href="https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls" rel="nofollow">URI string</a> that identifies your database. Connecting to a database with a URI caches the returned dataset. The URI string differs for each database dialect, so be sure to check the <a href="https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls" rel="nofollow">Database URLs</a> for whichever database you’re using.',Dt,B,bs="For SQLite, it is:",St,W,Vt,P,_s='Load the table by passing the table name and URI to <a href="/docs/datasets/pr_7865/en/package_reference/main_classes#datasets.Dataset.from_sql">from_sql()</a>:',Et,A,Bt,K,xs='Then you can use all of 🤗 Datasets process features like <a href="/docs/datasets/pr_7865/en/package_reference/main_classes#datasets.Dataset.filter">filter()</a> for example:',Wt,O,Pt,tt,Cs="You can also load a dataset from a SQL query instead of an entire table, which is useful for querying and joining multiple tables.",At,st,vs='Load the dataset by passing your query and URI to <a href="/docs/datasets/pr_7865/en/package_reference/main_classes#datasets.Dataset.from_sql">from_sql()</a>:',Kt,at,Ot,et,Rs='Then you can use all of 🤗 Datasets process features like <a href="/docs/datasets/pr_7865/en/package_reference/main_classes#datasets.Dataset.filter">filter()</a> for example:',ts,lt,ss,nt,as,pt,Us='You can also connect and load a dataset from a PostgreSQL database, however we won’t directly demonstrate how in the documentation because the example is only meant to be run in a notebook. Instead, take a look at how to install and setup a PostgreSQL server in this <a href="https://colab.research.google.com/github/nateraw/huggingface-hub-examples/blob/main/sql_with_huggingface_datasets.ipynb#scrollTo=d83yGQMPHGFi" rel="nofollow">notebook</a>!',es,it,qs='After you’ve setup your PostgreSQL database, you can use the <a href="/docs/datasets/pr_7865/en/package_reference/main_classes#datasets.Dataset.from_sql">from_sql()</a> method to load a dataset from a table or query.',ls,rt,ns,mt,ps;return M=new zs({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),y=new ot({props:{title:"Load tabular data",local:"load-tabular-data",headingTag:"h1"}}),w=new ot({props:{title:"CSV files",local:"csv-files",headingTag:"h2"}}),T=new u({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJjc3YlMjIlMkMlMjBkYXRhX2ZpbGVzJTNEJTIybXlfZmlsZS5jc3YlMjIpJTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJjc3YlMjIlMkMlMjBkYXRhX2ZpbGVzJTNEJTVCJTIybXlfZmlsZV8xLmNzdiUyMiUyQyUyMCUyMm15X2ZpbGVfMi5jc3YlMjIlMkMlMjAlMjJteV9maWxlXzMuY3N2JTIyJTVEKQ==",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
	<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"csv"</span>, data_files=<span class="hljs-string">"my_file.csv"</span>)

	<span class="hljs-comment"># load multiple CSV files</span>
	<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"csv"</span>, data_files=[<span class="hljs-string">"my_file_1.csv"</span>, <span class="hljs-string">"my_file_2.csv"</span>, <span class="hljs-string">"my_file_3.csv"</span>])`,wrap:!1}}),_=new u({props:{code:"ZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJjc3YlMjIlMkMlMjBkYXRhX2ZpbGVzJTNEJTdCJTIydHJhaW4lMjIlM0ElMjAlNUIlMjJteV90cmFpbl9maWxlXzEuY3N2JTIyJTJDJTIwJTIybXlfdHJhaW5fZmlsZV8yLmNzdiUyMiU1RCUyQyUyMCUyMnRlc3QlMjIlM0ElMjAlMjJteV90ZXN0X2ZpbGUuY3N2JTIyJTdEKQ==",highlighted:'<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"csv"</span>, data_files={<span class="hljs-string">"train"</span>: [<span class="hljs-string">"my_train_file_1.csv"</span>, <span class="hljs-string">"my_train_file_2.csv"</span>], <span class="hljs-string">"test"</span>: <span class="hljs-string">"my_test_file.csv"</span>})',wrap:!1}}),C=new u({props:{code:"YmFzZV91cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZsaG9lc3RxJTJGZGVtbzElMkZyZXNvbHZlJTJGbWFpbiUyRmRhdGElMkYlMjIlMEFkYXRhc2V0JTIwJTNEJTIwbG9hZF9kYXRhc2V0KCdjc3YnJTJDJTIwZGF0YV9maWxlcyUzRCU3QiUyMnRyYWluJTIyJTNBJTIwYmFzZV91cmwlMjAlMkIlMjAlMjJ0cmFpbi5jc3YlMjIlMkMlMjAlMjJ0ZXN0JTIyJTNBJTIwYmFzZV91cmwlMjAlMkIlMjAlMjJ0ZXN0LmNzdiUyMiU3RCk=",highlighted:`<span class="hljs-meta">>>> </span>base_url = <span class="hljs-string">"https://huggingface.co/datasets/lhoestq/demo1/resolve/main/data/"</span>
	<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">'csv'</span>, data_files={<span class="hljs-string">"train"</span>: base_url + <span class="hljs-string">"train.csv"</span>, <span class="hljs-string">"test"</span>: base_url + <span class="hljs-string">"test.csv"</span>})`,wrap:!1}}),R=new u({props:{code:"dXJsJTIwJTNEJTIwJTIyaHR0cHMlM0ElMkYlMkZkb21haW4ub3JnJTJGdHJhaW5fZGF0YS56aXAlMjIlMEFkYXRhX2ZpbGVzJTIwJTNEJTIwJTdCJTIydHJhaW4lMjIlM0ElMjB1cmwlN0QlMEFkYXRhc2V0JTIwJTNEJTIwbG9hZF9kYXRhc2V0KCUyMmNzdiUyMiUyQyUyMGRhdGFfZmlsZXMlM0RkYXRhX2ZpbGVzKQ==",highlighted:`<span class="hljs-meta">>>> </span>url = <span class="hljs-string">"https://domain.org/train_data.zip"</span>
	<span class="hljs-meta">>>> </span>data_files = {<span class="hljs-string">"train"</span>: url}
	<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"csv"</span>, data_files=data_files)`,wrap:!1}}),U=new ot({props:{title:"Pandas DataFrames",local:"pandas-dataframes",headingTag:"h2"}}),k=new u({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwRGF0YXNldCUwQWltcG9ydCUyMHBhbmRhcyUyMGFzJTIwcGQlMEElMEFkZiUyMCUzRCUyMHBkLnJlYWRfY3N2KCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZkYXRhc2V0cyUyRmltb2RlbHMlMkZjcmVkaXQtY2FyZCUyRnJhdyUyRm1haW4lMkZ0cmFpbi5jc3YlMjIpJTBBZGYlMjAlM0QlMjBwZC5EYXRhRnJhbWUoZGYpJTBBZGF0YXNldCUyMCUzRCUyMERhdGFzZXQuZnJvbV9wYW5kYXMoZGYp",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd

	<span class="hljs-comment"># create a Pandas DataFrame</span>
	<span class="hljs-meta">>>> </span>df = pd.read_csv(<span class="hljs-string">"https://huggingface.co/datasets/imodels/credit-card/raw/main/train.csv"</span>)
	<span class="hljs-meta">>>> </span>df = pd.DataFrame(df)
	<span class="hljs-comment"># load Dataset from Pandas DataFrame</span>
	<span class="hljs-meta">>>> </span>dataset = Dataset.from_pandas(df)`,wrap:!1}}),G=new u({props:{code:"dHJhaW5fZHMlMjAlM0QlMjBEYXRhc2V0LmZyb21fcGFuZGFzKHRyYWluX2RmJTJDJTIwc3BsaXQlM0QlMjJ0cmFpbiUyMiklMEF0ZXN0X2RzJTIwJTNEJTIwRGF0YXNldC5mcm9tX3BhbmRhcyh0ZXN0X2RmJTJDJTIwc3BsaXQlM0QlMjJ0ZXN0JTIyKQ==",highlighted:`<span class="hljs-meta">>>> </span>train_ds = Dataset.from_pandas(train_df, split=<span class="hljs-string">"train"</span>)
	<span class="hljs-meta">>>> </span>test_ds = Dataset.from_pandas(test_df, split=<span class="hljs-string">"test"</span>)`,wrap:!1}}),I=new ot({props:{title:"HDF5 files",local:"hdf5-files",headingTag:"h2"}}),F=new u({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJoZGY1JTIyJTJDJTIwZGF0YV9maWxlcyUzRCUyMmRhdGEuaDUlMjIp",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
	<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"hdf5"</span>, data_files=<span class="hljs-string">"data.h5"</span>)`,wrap:!1}}),H=new ot({props:{title:"Databases",local:"databases",headingTag:"h2"}}),L=new ot({props:{title:"SQLite",local:"sqlite",headingTag:"h3"}}),S=new u({props:{code:"aW1wb3J0JTIwc3FsaXRlMyUwQWltcG9ydCUyMHBhbmRhcyUyMGFzJTIwcGQlMEElMEFjb25uJTIwJTNEJTIwc3FsaXRlMy5jb25uZWN0KCUyMnVzX2NvdmlkX2RhdGEuZGIlMjIpJTBBZGYlMjAlM0QlMjBwZC5yZWFkX2NzdiglMjJodHRwcyUzQSUyRiUyRnJhdy5naXRodWJ1c2VyY29udGVudC5jb20lMkZueXRpbWVzJTJGY292aWQtMTktZGF0YSUyRm1hc3RlciUyRnVzLXN0YXRlcy5jc3YlMjIpJTBBZGYudG9fc3FsKCUyMnN0YXRlcyUyMiUyQyUyMGNvbm4lMkMlMjBpZl9leGlzdHMlM0QlMjJyZXBsYWNlJTIyKQ==",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> sqlite3
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd

	<span class="hljs-meta">>>> </span>conn = sqlite3.connect(<span class="hljs-string">"us_covid_data.db"</span>)
	<span class="hljs-meta">>>> </span>df = pd.read_csv(<span class="hljs-string">"https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"</span>)
	<span class="hljs-meta">>>> </span>df.to_sql(<span class="hljs-string">"states"</span>, conn, if_exists=<span class="hljs-string">"replace"</span>)`,wrap:!1}}),W=new u({props:{code:"dXJpJTIwJTNEJTIwJTIyc3FsaXRlJTNBJTJGJTJGJTJGdXNfY292aWRfZGF0YS5kYiUyMg==",highlighted:'<span class="hljs-meta">>>> </span>uri = <span class="hljs-string">"sqlite:///us_covid_data.db"</span>',wrap:!1}}),A=new u({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwRGF0YXNldCUwQSUwQWRzJTIwJTNEJTIwRGF0YXNldC5mcm9tX3NxbCglMjJzdGF0ZXMlMjIlMkMlMjB1cmkpJTBBZHM=",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset

	<span class="hljs-meta">>>> </span>ds = Dataset.from_sql(<span class="hljs-string">"states"</span>, uri)
	<span class="hljs-meta">>>> </span>ds
	Dataset({
	features: [<span class="hljs-string">'index'</span>, <span class="hljs-string">'date'</span>, <span class="hljs-string">'state'</span>, <span class="hljs-string">'fips'</span>, <span class="hljs-string">'cases'</span>, <span class="hljs-string">'deaths'</span>],
	num_rows: <span class="hljs-number">54382</span>
	})`,wrap:!1}}),O=new u({props:{code:"ZHMuZmlsdGVyKGxhbWJkYSUyMHglM0ElMjB4JTVCJTIyc3RhdGUlMjIlNUQlMjAlM0QlM0QlMjAlMjJDYWxpZm9ybmlhJTIyKQ==",highlighted:'<span class="hljs-meta">>>> </span>ds.<span class="hljs-built_in">filter</span>(<span class="hljs-keyword">lambda</span> x: x[<span class="hljs-string">"state"</span>] == <span class="hljs-string">"California"</span>)',wrap:!1}}),at=new u({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwRGF0YXNldCUwQSUwQWRzJTIwJTNEJTIwRGF0YXNldC5mcm9tX3NxbCgnU0VMRUNUJTIwKiUyMEZST00lMjBzdGF0ZXMlMjBXSEVSRSUyMHN0YXRlJTNEJTIyQ2FsaWZvcm5pYSUyMiUzQiclMkMlMjB1cmkpJTBBZHM=",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset

	<span class="hljs-meta">>>> </span>ds = Dataset.from_sql(<span class="hljs-string">'SELECT * FROM states WHERE state="California";'</span>, uri)
	<span class="hljs-meta">>>> </span>ds
	Dataset({
	features: [<span class="hljs-string">'index'</span>, <span class="hljs-string">'date'</span>, <span class="hljs-string">'state'</span>, <span class="hljs-string">'fips'</span>, <span class="hljs-string">'cases'</span>, <span class="hljs-string">'deaths'</span>],
	num_rows: <span class="hljs-number">1019</span>
	})`,wrap:!1}}),lt=new u({props:{code:"ZHMuZmlsdGVyKGxhbWJkYSUyMHglM0ElMjB4JTVCJTIyY2FzZXMlMjIlNUQlMjAlM0UlMjAxMDAwMCk=",highlighted:'<span class="hljs-meta">>>> </span>ds.<span class="hljs-built_in">filter</span>(<span class="hljs-keyword">lambda</span> x: x[<span class="hljs-string">"cases"</span>] > <span class="hljs-number">10000</span>)',wrap:!1}}),nt=new ot({props:{title:"PostgreSQL",local:"postgresql",headingTag:"h3"}}),rt=new 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Xet Storage Details

Size:: 23.5 kB
Xet hash:: 875f67ea0cf7a9cfb3d3986946d709379224f4154b5d14794129e880daefbdcd

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.