Danfo.js
  • Danfo.js Documentation
  • Getting Started
  • API reference
    • General Functions
      • danfo.tensorflow
      • danfo. convertFunctionTotransformer
      • danfo.streamCsvTransformer
      • danfo.streamJSON
      • danfo.streamCSV
      • danfo.Utils
      • danfo.Str
      • danfo.Dt
      • danfo.dateRange
      • danfo.OneHotEncoder
      • danfo.StandardScaler
      • danfo.MinMaxScaler
      • danfo.LabelEncoder
      • danfo.toDateTime
      • danfo.getDummies
      • danfo.concat
      • danfo.merge
    • Input/Output
      • danfo.readExcel
      • danfo.toExcel
      • danfo.readJSON
      • danfo.toJSON
      • danfo.readCSV
      • danfo.toCSV
    • Series
      • Creating a Series
      • Series.append
      • Series.cumSum
      • Series.cumMax
      • Series.cumProd
      • Series.cumMin
      • Series.str.split
      • Series.str.len
      • Series.str.join
      • Series.str.trim
      • Series.str.substring
      • Series.str.substr
      • Series.str.slice
      • Series.str.search
      • Series.str.repeat
      • Series.str.replace
      • Series.str.lastIndexOf
      • Series.str.indexOf
      • Series.str.includes
      • Series.str.endsWith
      • Series.str.startsWith
      • Series.str.concat
      • Series.str.charAt
      • Series.str.toUpperCase
      • Series.str.toLowerCase
      • Series.str.capitalize
      • Series.dt.seconds
      • Series.dt.minutes
      • Series.dt.dayOfMonth
      • Series.dt.monthName
      • Series.dt.hours
      • Series.dt.dayOfWeek
      • Series.dt.dayOfWeek
      • Series.dt.month
      • Series.dt.year
      • Series.argMax
      • Series.argMin
      • Series.argSort
      • Series.replace
      • Series.isNa
      • Series.fillNa
      • Series.dropNa
      • Series.dropDuplicates
      • Series.valueCounts
      • Series.nUnique
      • Series.unique
      • Series.abs
      • Series.ne
      • Series.eq
      • Series.ge
      • Series.le
      • Series.gt
      • Series.lt
      • Series.iloc
      • Series.loc
      • Series.at
      • Series.iat
      • Series.ndim
      • Series.shape
      • Series.dtype
      • Series.values
      • Series.tensor
      • Series.index
      • Series.apply
      • Series.map
      • Series.setIndex
      • Series.resetIndex
      • Series.describe
      • Series.copy
      • Series.sortValues
      • Series.var
      • Series.std
      • Series.round
      • Series.minimum
      • Series.maximum
      • Series.count
      • Series.sum
      • Series.max
      • Series.min
      • Series.mode
      • Series.median
      • Series.mean
      • Series.mod
      • Series.pow
      • Series.div
      • Series.mul
      • Series.sub
      • Series.add
      • Series.sample
      • Series.tail
      • Series.head
      • Series.and
      • Series.or
    • Dataframe
      • Creating a DataFrame
      • DataFrame.sortIndex
      • DataFrame.append
      • DataFrame.nUnique
      • DataFrame.tensor
      • DataFrame.print
      • DataFrame.toCSV
      • DataFrame.toJSON
      • DataFrame.toExcel
      • DataFrame.sortValues
      • DataFrame.setIndex
      • DataFrame.resetIndex
      • DataFrame.rename
      • DataFrame.drop
      • DataFrame.asType
      • DataFrame.shape
      • DataFrame.axis
      • DataFrame.ndim
      • DataFrame.values
      • DataFrame.selectDtypes
      • DataFrame.ctypes
      • DataFrame.index
      • DataFrame.loc
      • DataFrame.iloc
      • DataFrame.at
      • DataFrame.iat
      • DataFrame.head
      • DataFrame.tail
      • DataFrame.sample
      • DataFrame.add
      • DataFrame.sub
      • DataFrame.mul
      • DataFrame.div
      • DataFrame.pow
      • DataFrame.mod
      • DataFrame.mean
      • DataFrame.median
      • DataFrame.min
      • DataFrame.max
      • DataFrame.std
      • DataFrame.var
      • DataFrame.count
      • DataFrame.round
      • DataFrame.cumSum
      • DataFrame.cumMin
      • DataFrame.cumMax
      • DataFrame.cumProd
      • DataFrame.copy
      • DataFrame.describe
      • DataFrame.sum
      • DataFrame.abs
      • DataFrame.query
      • DataFrame.addColumn
      • DataFrame.groupby
      • DataFrame.column
      • DataFrame.fillNa
      • DataFrame.isNa
      • DataFrame.dropNa
      • DataFrame.apply
      • DataFrame.applyMap
      • DataFrame.It
      • DataFrame.gt
      • DataFrame.le
      • DataFrame.ge
      • DataFrame.ne
      • DataFrame.eq
      • DataFrame.replace
    • Configuration Options
    • Plotting
      • Timeseries Plots
      • Violin Plots
      • Box Plots
      • Tables
      • Pie Charts
      • Histograms
      • Scatter Plots
      • Bar Charts
      • Line Charts
      • Customizing your plots
    • Groupby
      • Groupby.getGroups
      • Groupby.col
      • Groupby.max
      • Groupby.min
      • Groupby.sum
      • Groupby.mean
      • Groupby.std
      • Groupby.var
      • Groupby.count
      • Groupby.cumSum
      • Groupby.cumMax
      • Groupby.cumMin
      • Groupby.cumProd
      • Groupby.agg
  • User Guides
    • Migrating to the stable version of Danfo.js
    • Using Danfojs in React
    • Titanic Survival Prediction using Danfo.js and Tensorflow.js
  • Building Data Driven Applications with Danfo.js - Book
  • Contributing Guide
  • Release Notes
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On this page
  • Reading files from local disk
  • Reading files from a URL
  • Reading an input file object in the browser

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  1. API reference
  2. Input/Output

danfo.readCSV

Reads a comma-separated values (CSV) file into DataFrame. Also supports the reading of CSV files in chunks.

danfo.readCSV(source, options)

Parameters

Type

Description

Default

source

File object, File path, URL

Any valid string path is acceptable. The string could be a URL or a valid local file path.

options

object, optional

{

header: true

}

The readCSV method can read a CSV file from a local disk, or over the internet (URL). Reading of local files is only supported in Nodejs, while reading of input file objects is only supported in the browser.

Reading files from local disk

By specifying a valid file path, you can load CSV files from local disk:

const dfd = require("danfojs-node")

dfd.readCSV("./user_names.csv") //assumes file is in CWD
  .then(df => {
  
   df.head().print()

  }).catch(err=>{
     console.log(err);
  })

Reading files from a URL

By specifying a valid URL, you can load CSV files from any location into Danfo**'**s data structure:

const dfd = require("danfojs-node")

dfd.readCSV("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv") //assumes file is in CWD
  .then(df => {
  
   df.head().print()

  }).catch(err=>{
     console.log(err);
  })
<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <script src="https://cdn.plot.ly/plotly-2.2.0.min.js"></script> 
     <script src="https://cdn.jsdelivr.net/npm/danfojs@1.2.0/lib/bundle.min.js"></script>
    <title>Document</title>
</head>

<body>

    <div id="plot_div"></div>
    <script>

         dfd.readCSV("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
            .then(df => {

                //do something like display descriptive statistics
                df.describe().print()
                
            }).catch(err => {
                console.log(err);
            })
         
    </script>
</body>

</html>

Reading an input file object in the browser

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <script src="https://cdn.jsdelivr.net/npm/danfojs@1.2.0/lib/bundle.min.js"></script>
    <title>Document</title>
</head>

<body>
    <input type="file" id="file" name="file">
    <script>
            
        inputFile.addEventListener("change", async () => {
            const csvFile = inputFile.files[0]
            dfd.readCSV(csvFile).then((df) => {
                df.print()
            })
        })
         
    </script>
</body>

</html>
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Last updated 2 months ago

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A browser is also supported.

Supports all Papaparse config parameters. See .

By specifying a valid , you can load CSV files in the browser in DataFrames/Series

file object
input file object
https://www.papaparse.com/docs#config