diff --git a/.gitignore b/.gitignore
index d2fea82..0ec9a24 100644
--- a/.gitignore
+++ b/.gitignore
@@ -132,3 +132,4 @@ Cargo.lock
 # test files
 testfiles/*.csv
 !testfiles/generated-100.csv
+!testfiles/err-*-col*.csv
diff --git a/Cargo.toml b/Cargo.toml
index 756019f..49bad94 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -11,10 +11,10 @@ crate-type = ["cdylib"]
 [dependencies]
 anyhow      = "1.0.102"
 csv         = "1.4.0"
-napi        = { version = "3.0.0", features = ["anyhow", "serde_json"] }
+itoa        = "1.0.18"
+napi        = { version = "3.0.0", features = ["anyhow"] }
 napi-derive = "3.0.0"
-serde       = "1.0.228"
-serde_json  = "1.0.149"
+ryu         = "1.0.23"
 
 [build-dependencies]
 napi-build = "2"
diff --git a/LICENSE b/LICENSE
index 6c7f562..1817579 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 N-API for Rust
+Copyright (c) 2026 Clelia Astra Bertelli
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index 8092a69..649ee13 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,8 @@ npm install @cle-does-things/sunbears
 
 ## Usage
 
+### `readCsv`
+
 The main function for `sunbears` is `readCsv`, which loads the data contained in a CSV file as a `DataFrame`, a columnar data format.
 
 ```typescript
@@ -24,6 +26,7 @@ The `DataFrame` class exposes two methods:
 
 - `colDtype`: retrieve the data type of the records contained within a column (integer, float, boolean or string)
 - `get`: get a column
+- `writeCsv`: write the dataframe to CSV (see the next paragraph)
 
 ```typescript
 const dt = df.colDtype('name')
@@ -61,36 +64,88 @@ const filteredNames = asStringArray(readCsv('test.csv').get('name'))?.filter((n)
 const mappedNames = asStringArray(readCsv('test.csv').get('name'))?.map((n) => n.toUpperCase())
 ```
 
-## Benchmarking
+### `DataFrame.writeCsv`
 
-`sunbears` was benchmarked using the `tinybench`-based script you can find [here](./benchmark/bench.ts). The script reports latency statistics related to the `readCsv` function reading increasingly large CSV files (100, 1000, 100.000 and 1.000.000 rows).
+The `writeCsv` method writes a DataFrame to CSV.
 
-The latest benchmark run was:
+You can construct a DataFrame simply starting from arrays, using the following helper functions:
 
-| Task                     | Latency avg (s)  | Latency med (s)      | Throughput avg (ops/s) | Throughput med (ops/s) | Samples |
-| ------------------------ | ---------------- | -------------------- | ---------------------- | ---------------------- | ------- |
-| Read a 100-lines CSV     | 0.000073 ± 0.20% | 0.000073 ± 0.0000015 | 13814 ± 0.11%          | 13841 ± 285            | 13727   |
-| Read a 1000-lines CSV    | 0.000423 ± 0.47% | 0.000412 ± 0.000010  | 2383 ± 0.31%           | 2427 ± 60              | 2362    |
-| Read a 100000-lines CSV  | 0.041591 ± 2.07% | 0.040895 ± 0.001126  | 24 ± 1.86%             | 24 ± 1                 | 64      |
-| Read a 1000000-lines CSV | 0.424404 ± 0.73% | 0.421294 ± 0.008276  | 2 ± 0.70%              | 2 ± 0                  | 64      |
+```typescript
+import { DataFrame, toIntColumn, toFloatColumn, toStringColumn, toBoolColumn } from '@cle-does-things/sunbears'
 
-Here is how the tool compares to the `read_csv` function in Pandas and Polars ([script](./testfiles/comparative_bench.py)):
+const col1 = toStringColumn(['hello', 'world'])
+const col2 = toFloatColumn([1.2, 2.3])
+const col3 = toIntColumn([4, 5])
+const col4 = toBoolColumn([true, false])
+```
 
-| Dataset       | Pandas (s) | Polars (s) |
-| ------------- | ---------- | ---------- |
-| 100 lines     | 0.030637   | 0.022748   |
-| 1000 lines    | 0.033386   | 0.017368   |
-| 100000 lines  | 0.453596   | 0.027075   |
-| 1000000 lines | 3.986837   | 0.198708   |
+You can then use the `fromColumns` factory for the `DataFrame` class to turn column data into a DataFrame: if the columns do not have the same length, an error will be thrown.
 
-And here it how it compares with `csv-parse` ([script](./benchmark/bench-alt.ts)):
+```typescript
+const df = DataFrame.fromColumns({
+  col1: col1,
+  col2: col2,
+  col3: col3,
+  col4: col4,
+})
+```
+
+Writing to the CSV file is then trivial:
+
+```typescript
+df.writeCsv('test.csv')
+```
 
-| Task                       | Latency avg (s)  | Latency med (s)     | Throughput avg (ops/s) | Throughput med (ops/s) | Samples |
-| -------------------------- | ---------------- | ------------------- | ---------------------- | ---------------------- | ------- |
-| Read a 100-lines CSV       | 0.000188 ± 0.98% | 0.000179 ± 0.000003 | 5,442 ± 0.23%          | 5,580 ± 94             | 5,312   |
-| Read a 1,000-lines CSV     | 0.001235 ± 0.69% | 0.001189 ± 0.000032 | 816 ± 0.58%            | 841 ± 23               | 810     |
-| Read a 100,000-lines CSV   | 0.11656 ± 0.26%  | 0.11648 ± 0.000642  | 9 ± 0.25%              | 9 ± 0                  | 64      |
-| Read a 1,000,000-lines CSV | 1.19114 ± 0.37%  | 1.18707 ± 0.007191  | 1 ± 0.36%              | 1 ± 0                  | 64      |
+The file will look like this:
+
+```csv
+col1,col2,col3,col4
+hello,1.2,4,true
+world,2.3,5,false
+```
+
+## Benchmarking
+
+`sunbears` was benchmarked using the `tinybench`-based script you can find [here](./benchmark/bench.ts). The script reports latency statistics related to the `readCsv` and `writeCsv` functions reading/writing increasingly large CSV files (100, 1000, 100.000 and 1.000.000 rows).
+
+The latest benchmark run was:
+
+| Task                      | Latency avg (s)  | Latency med (s)      | Throughput avg (ops/s) | Throughput med (ops/s) | Samples |
+| ------------------------- | ---------------- | -------------------- | ---------------------- | ---------------------- | ------- |
+| Read a 100-lines CSV      | 0.000054 ± 0.24% | 0.000050 ± 0.0000023 | 18964 ± 0.16%          | 19967 ± 921            | 18654   |
+| Read a 1000-lines CSV     | 0.000289 ± 0.54% | 0.000279 ± 0.0000090 | 3518 ± 0.32%           | 3583 ± 116             | 3464    |
+| Read a 100000-lines CSV   | 0.028000 ± 1.38% | 0.027537 ± 0.000254  | 36 ± 1.19%             | 36 ± 0                 | 64      |
+| Read a 1000000-lines CSV  | 0.310751 ± 0.75% | 0.308330 ± 0.004228  | 3 ± 0.70%              | 3 ± 0                  | 64      |
+| Write a 100-lines CSV     | 0.000076 ± 0.46% | 0.000069 ± 0.0000052 | 13665 ± 0.28%          | 14467 ± 1148           | 13140   |
+| Write a 1000-lines CSV    | 0.000213 ± 0.43% | 0.000209 ± 0.0000056 | 4724 ± 0.16%           | 4785 ± 130             | 4700    |
+| Write a 100000-lines CSV  | 0.013886 ± 0.86% | 0.013756 ± 0.000171  | 72 ± 0.78%             | 73 ± 1                 | 73      |
+| Write a 1000000-lines CSV | 0.146282 ± 1.39% | 0.146108 ± 0.005752  | 7 ± 1.31%              | 7 ± 0                  | 64      |
+
+Here is how the tool compares to the `read_csv` and `to_csv`/`write_csv` functions in Pandas and Polars ([script](./testfiles/comparative_bench.py)):
+
+| Dataset             | Pandas (s) | Polars (s) |
+| ------------------- | ---------- | ---------- |
+| Read 100 lines      | 0.038291   | 0.033831   |
+| Read 1000 lines     | 0.037794   | 0.016517   |
+| Read 100000 lines   | 0.471109   | 0.029076   |
+| Read 1000000 lines  | 4.153507   | 0.216254   |
+| Write 100 lines     | 0.035926   | 0.043052   |
+| Write 1000 lines    | 0.067816   | 0.017617   |
+| Write 100000 lines  | 0.892885   | 0.031329   |
+| Write 1000000 lines | 8.549390   | 0.331897   |
+
+And here it how it compares with `csv-parse` and `csv-stringify`+`writeFileSync` ([script](./benchmark/bench-alt.ts)):
+
+| Task                      | Latency avg (s)  | Latency med (s)      | Throughput avg (ops/s) | Throughput med (ops/s) | Samples |
+| ------------------------- | ---------------- | -------------------- | ---------------------- | ---------------------- | ------- |
+| Read a 100-lines CSV      | 0.000207 ± 2.31% | 0.000191 ± 0.0000087 | 5086 ± 0.31%           | 5224 ± 244             | 4842    |
+| Read a 1000-lines CSV     | 0.001244 ± 0.42% | 0.001233 ± 0.0000229 | 806 ± 0.33%            | 811 ± 15               | 805     |
+| Read a 100000-lines CSV   | 0.120565 ± 0.63% | 0.119515 ± 0.001141  | 8 ± 0.60%              | 8 ± 0                  | 64      |
+| Read a 1000000-lines CSV  | 1.216019 ± 0.46% | 1.209978 ± 0.006709  | 1 ± 0.44%              | 1 ± 0                  | 64      |
+| Write a 100-lines CSV     | 0.000087 ± 0.52% | 0.000080 ± 0.0000078 | 12010 ± 0.31%          | 12526 ± 1267           | 11503   |
+| Write a 1000-lines CSV    | 0.000290 ± 1.14% | 0.000275 ± 0.0000192 | 3555 ± 0.42%           | 3635 ± 258             | 3451    |
+| Write a 100000-lines CSV  | 0.027303 ± 2.96% | 0.027014 ± 0.000900  | 37 ± 1.87%             | 37 ± 1                 | 64      |
+| Write a 1000000-lines CSV | 0.273814 ± 2.08% | 0.265154 ± 0.005824  | 4 ± 1.77%              | 4 ± 0                  | 64      |
 
 ## Development
 
@@ -176,3 +231,7 @@ git push
 GitHub actions will do the rest job for you.
 
 > WARN: Don't run `npm publish` manually.
+
+## License
+
+MIT
diff --git a/__test__/index.spec.ts b/__test__/index.spec.ts
index 5f442bb..3f62df5 100644
--- a/__test__/index.spec.ts
+++ b/__test__/index.spec.ts
@@ -9,7 +9,12 @@ import {
   asFloatArray,
   asIntArray,
   asStringArray,
+  toIntColumn,
+  toFloatColumn,
+  toStringColumn,
+  toBoolColumn,
 } from '../index'
+import { unlinkSync } from 'fs'
 
 test('readCsv reads a CSV and returns a DataFrame with correct datatypes', (t) => {
   const csvPath = 'testfiles/generated-100.csv'
@@ -36,6 +41,23 @@ test('readCsv reads a CSV and returns a DataFrame with correct datatypes', (t) =
   t.truthy(df.get('years_of_experience'))
 })
 
+test('readCsv throws expected type-dependent errors', (t) => {
+  t.throws(
+    () => {
+      readCsv('testfiles/err-typed-col-1.csv')
+    },
+    undefined,
+    'Expecting integer at line 2 for column colErr',
+  )
+  t.throws(
+    () => {
+      readCsv('testfiles/err-typed-col-2.csv')
+    },
+    undefined,
+    'Expecting integer at line 2 for column colErr',
+  )
+})
+
 test('DataFrame class methods work correctly', (t) => {
   const columns: Record<string, ColumnData> = {
     test: { type: 'String', field0: ['hello', 'world'] },
@@ -56,7 +78,7 @@ test('DataFrame class methods work correctly', (t) => {
   t.deepEqual(df.get('last_one'), columns['last_one'])
 })
 
-test('Column to array functions work', (t) => {
+test('Column to array functions work correctly', (t) => {
   const columns: Record<string, ColumnData> = {
     name: { type: 'String', field0: ['hello', 'world'] },
     other: { type: 'Boolean', field0: [false, true] },
@@ -80,3 +102,90 @@ test('Column to array functions work', (t) => {
   t.truthy(lastArray)
   t.deepEqual(lastArray, columns['last_one'].field0)
 })
+
+test('Array to column functions work correctly', (t) => {
+  const intArr = [0, 1, 2, 3]
+  const floatArr = [0.1, 1.2, 2.3, 3.4]
+  const stringArr = ['hello', 'world', 'how', 'are']
+  const boolArr = [true, true, false, false]
+  const intCol = toIntColumn(intArr)
+  t.deepEqual(intCol.field0, intArr)
+  t.is(intCol.type, 'Integer')
+  const floatCol = toFloatColumn(floatArr)
+  t.deepEqual(floatCol.field0, floatArr)
+  t.is(floatCol.type, 'Float')
+  const stringCol = toStringColumn(stringArr)
+  t.deepEqual(stringCol.field0, stringArr)
+  t.is(stringCol.type, 'String')
+  const boolCol = toBoolColumn(boolArr)
+  t.deepEqual(boolCol.field0, boolArr)
+  t.is(boolCol.type, 'Boolean')
+})
+
+test('DataFrame constructor and factory work correctly', (t) => {
+  const columns: Record<string, ColumnData> = {
+    name: { type: 'String', field0: ['hello', 'world'] },
+    other: { type: 'Boolean', field0: [false, true] },
+    another: { type: 'Integer', field0: [1, 2] },
+    last_one: { type: 'Float', field0: [0.3, 2.6] },
+  }
+  const df = new DataFrame(columns, 2)
+  t.is(df.len, 2)
+  for (const k of Object.keys(columns)) {
+    t.deepEqual(df.get(k), columns[k])
+  }
+  const df1 = DataFrame.fromColumns(columns)
+  t.is(df1.len, 2)
+  for (const k of Object.keys(columns)) {
+    t.deepEqual(df1.get(k), columns[k])
+  }
+})
+
+test('DataFrame constructor and factory throw expected errors', (t) => {
+  const columns: Record<string, ColumnData> = {
+    name: { type: 'String', field0: ['hello', 'world'] },
+    other: { type: 'Boolean', field0: [false, true] },
+    another: { type: 'Integer', field0: [1, 2] },
+    last_one: { type: 'Float', field0: [0.3, 2.6] },
+  }
+  const columns1: Record<string, ColumnData> = {
+    name: { type: 'String', field0: ['hello'] },
+    other: { type: 'Boolean', field0: [false, true] },
+    another: { type: 'Integer', field0: [1, 2] },
+    last_one: { type: 'Float', field0: [0.3, 2.6] },
+  }
+  t.throws(
+    () => {
+      new DataFrame(columns, 3)
+    },
+    undefined,
+    'Not all columns are of the declared length',
+  )
+  t.throws(
+    () => {
+      DataFrame.fromColumns(columns1)
+    },
+    undefined,
+    'Not all columns are the same length',
+  )
+})
+
+test('Write to CSV works correctly', (t) => {
+  const col1 = toStringColumn(['hello', 'world'])
+  const col2 = toFloatColumn([1.2, 2.3])
+  const col3 = toIntColumn([4, 5])
+  const col4 = toBoolColumn([true, false])
+  const dfw = DataFrame.fromColumns({
+    col1: col1,
+    col2: col2,
+    col3: col3,
+    col4: col4,
+  })
+  dfw.writeCsv('testfiles/write-test.csv')
+  const dfr = readCsv('testfiles/write-test.csv')
+  const colw = dfw.columns
+  for (const k of Object.keys(colw)) {
+    t.deepEqual(dfr.get(k), colw[k])
+  }
+  unlinkSync('testfiles/write-test.csv')
+})
diff --git a/benchmark/bench-alt.ts b/benchmark/bench-alt.ts
index 92d79c6..05e9187 100644
--- a/benchmark/bench-alt.ts
+++ b/benchmark/bench-alt.ts
@@ -1,28 +1,13 @@
+/* eslint-disable */
+
 import { Bench } from 'tinybench'
 import { parse } from 'csv-parse'
-import fs from 'node:fs'
+import fs, { unlinkSync, writeFileSync } from 'node:fs'
 import { finished } from 'stream/promises'
+import { stringify } from 'csv-stringify/sync'
 
 const b = new Bench()
 
-// const processFile = async () => {
-//     const records = [];
-//     const parser = fs.createReadStream(`${os.tmpdir()}/input.csv`).pipe(
-//       parse({
-//         // CSV options if any
-//       }),
-//     );
-//     parser.on("readable", function () {
-//       let record;
-//       while ((record = parser.read()) !== null) {
-//         // Work with each record
-//         records.push(record);
-//       }
-//     });
-//     await finished(parser);
-//     return records;
-//   };
-
 b.add('Read a 100-lines CSV', async () => {
   console.log('Started 100 lines bench')
   const parser = fs.createReadStream(`testfiles/generated-100.csv`).pipe(parse({ toLine: 100 }))
@@ -73,6 +58,143 @@ b.add('Read a 1000000-lines CSV', async () => {
   console.log('Ended 1.000.000 lines bench')
 })
 
+let records_100: any[][] | undefined
+let records_1000: any[][] | undefined
+let records_100000: any[][] | undefined
+let records_1000000: any[][] | undefined
+
+b.add(
+  'Write a 100-lines CSV',
+  async () => {
+    console.log('Started writing 100 lines bench')
+    const output = stringify(records_100!)
+    writeFileSync('testfiles/written-alt-100.csv', output)
+    console.log('Ended writing 100 lines bench')
+  },
+  {
+    beforeAll: () => {
+      const col1 = new Array(100).fill('something')
+      const col2 = new Array(100).fill(1.0)
+      const col3 = new Array(100).fill(3)
+      const col4 = new Array(100).fill(true)
+      const cols = [col1, col2, col3, col4]
+      let i = 0
+      const matrix: any[][] = []
+      while (i < 100) {
+        let row = []
+        for (const col of cols) {
+          row.push(col[i])
+        }
+        matrix.push(row)
+        i++
+      }
+      records_100 = matrix
+    },
+    afterEach: () => {
+      unlinkSync('testfiles/written-alt-100.csv')
+    },
+  },
+)
+
+b.add(
+  'Write a 1000-lines CSV',
+  async () => {
+    console.log('Started writing 1000 lines bench')
+    const output = stringify(records_1000!)
+    writeFileSync('testfiles/written-alt-1000.csv', output)
+    console.log('Ended writing 1000 lines bench')
+  },
+  {
+    beforeAll: () => {
+      const col1 = new Array(1000).fill('something')
+      const col2 = new Array(1000).fill(1.0)
+      const col3 = new Array(1000).fill(3)
+      const col4 = new Array(1000).fill(true)
+      const cols = [col1, col2, col3, col4]
+      let i = 0
+      const matrix: any[][] = []
+      while (i < 1000) {
+        const row = []
+        for (const col of cols) {
+          row.push(col[i])
+        }
+        matrix.push(row)
+        i++
+      }
+      records_1000 = matrix
+    },
+    afterEach: () => {
+      unlinkSync('testfiles/written-alt-1000.csv')
+    },
+  },
+)
+
+b.add(
+  'Write a 100.000-lines CSV',
+  async () => {
+    console.log('Started writing 100.000 lines bench')
+    const output = stringify(records_100000!)
+    writeFileSync('testfiles/written-alt-100000.csv', output)
+    console.log('Ended writing 100.000 lines bench')
+  },
+  {
+    beforeAll: () => {
+      const col1 = new Array(100000).fill('something')
+      const col2 = new Array(100000).fill(1.0)
+      const col3 = new Array(100000).fill(3)
+      const col4 = new Array(100000).fill(true)
+      const cols = [col1, col2, col3, col4]
+      let i = 0
+      const matrix: any[][] = []
+      while (i < 100000) {
+        const row = []
+        for (const col of cols) {
+          row.push(col[i])
+        }
+        matrix.push(row)
+        i++
+      }
+      records_100000 = matrix
+    },
+    afterEach: () => {
+      unlinkSync('testfiles/written-alt-100000.csv')
+    },
+  },
+)
+
+b.add(
+  'Write a 1.000.000-lines CSV',
+  async () => {
+    console.log('Started writing 1.000.000 lines bench')
+    const output = stringify(records_1000000!)
+    writeFileSync('testfiles/written-alt-1000000.csv', output)
+    console.log('Ended writing 1.000.000 lines bench')
+  },
+  {
+    beforeAll: () => {
+      const col1 = new Array(1000000).fill('something')
+      const col2 = new Array(1000000).fill(1.0)
+      const col3 = new Array(1000000).fill(3)
+      const col4 = new Array(1000000).fill(true)
+      const cols = [col1, col2, col3, col4]
+      let i = 0
+      const matrix: any[][] = []
+      while (i < 1000000) {
+        const row = []
+        for (const col of cols) {
+          row.push(col[i])
+        }
+        matrix.push(row)
+        i++
+      }
+      records_1000000 = matrix
+    },
+    afterEach: () => {
+      unlinkSync('testfiles/written-alt-1000000.csv')
+    },
+  },
+)
+
 await b.run()
 
 console.table(b.table())
diff --git a/benchmark/bench.ts b/benchmark/bench.ts
index dd47ba0..9069203 100644
--- a/benchmark/bench.ts
+++ b/benchmark/bench.ts
@@ -1,6 +1,9 @@
+/* eslint-disable */
+
 import { Bench } from 'tinybench'
 
-import { readCsv } from '../index.js'
+import { DataFrame, readCsv, toBoolColumn, toFloatColumn, toIntColumn, toStringColumn } from '../index.js'
+import { unlinkSync } from 'fs'
 
 const b = new Bench()
 
@@ -28,6 +31,115 @@ b.add('Read a 1000000-lines CSV', () => {
   console.log('Ended 1.000.000 lines bench')
 })
 
+let df: DataFrame | undefined = undefined
+let df_100: DataFrame | undefined = undefined
+let df_1000: DataFrame | undefined = undefined
+let df_100000: DataFrame | undefined = undefined
+
+b.add(
+  'Write a 100-lines CSV',
+  () => {
+    console.log('Started writing 100 lines bench')
+    df_100!.writeCsv('testfiles/written-100.csv')
+    console.log('Ended writing 100 lines bench')
+  },
+  {
+    beforeAll: () => {
+      const col1 = toStringColumn(new Array(100).fill('something'))
+      const col2 = toFloatColumn(new Array(100).fill(1.0))
+      const col3 = toIntColumn(new Array(100).fill(3))
+      const col4 = toBoolColumn(new Array(100).fill(true))
+      df_100 = DataFrame.fromColumns({
+        col1: col1,
+        col2: col2,
+        col3: col3,
+        col4: col4,
+      })
+    },
+    afterEach: () => {
+      unlinkSync('testfiles/written-100.csv')
+    },
+  },
+)
+
+b.add(
+  'Write a 1000-lines CSV',
+  () => {
+    console.log('Started writing 1000 lines bench')
+    df_1000!.writeCsv('testfiles/written-1000.csv')
+    console.log('Ended writing 1000 lines bench')
+  },
+  {
+    beforeAll: () => {
+      const col1 = toStringColumn(new Array(1000).fill('something'))
+      const col2 = toFloatColumn(new Array(1000).fill(1.0))
+      const col3 = toIntColumn(new Array(1000).fill(3))
+      const col4 = toBoolColumn(new Array(1000).fill(true))
+      df_1000 = DataFrame.fromColumns({
+        col1: col1,
+        col2: col2,
+        col3: col3,
+        col4: col4,
+      })
+    },
+    afterEach: () => {
+      unlinkSync('testfiles/written-1000.csv')
+    },
+  },
+)
+
+b.add(
+  'Write a 100000-lines CSV',
+  () => {
+    console.log('Started writing 100.000 lines bench')
+    df_100000!.writeCsv('testfiles/written-100000.csv')
+    console.log('Ended writing 100.000 lines bench')
+  },
+  {
+    beforeAll: () => {
+      const col1 = toStringColumn(new Array(100_000).fill('something'))
+      const col2 = toFloatColumn(new Array(100_000).fill(1.0))
+      const col3 = toIntColumn(new Array(100_000).fill(3))
+      const col4 = toBoolColumn(new Array(100_000).fill(true))
+      df_100000 = DataFrame.fromColumns({
+        col1: col1,
+        col2: col2,
+        col3: col3,
+        col4: col4,
+      })
+    },
+    afterEach: () => {
+      unlinkSync('testfiles/written-100000.csv')
+    },
+  },
+)
+
+b.add(
+  'Write a 1000000-lines CSV',
+  () => {
+    console.log('Started writing 1.000.000 lines bench')
+    df!.writeCsv('testfiles/written-1000000.csv')
+    console.log('Ended writing 1.000.000 lines bench')
+  },
+  {
+    beforeAll: () => {
+      const col1 = toStringColumn(new Array(1_000_000).fill('something'))
+      const col2 = toFloatColumn(new Array(1_000_000).fill(1.0))
+      const col3 = toIntColumn(new Array(1_000_000).fill(3))
+      const col4 = toBoolColumn(new Array(1_000_000).fill(true))
+      df = DataFrame.fromColumns({
+        col1: col1,
+        col2: col2,
+        col3: col3,
+        col4: col4,
+      })
+    },
+    afterEach: () => {
+      unlinkSync('testfiles/written-1000000.csv')
+    },
+  },
+)
+
 await b.run()
 
 console.table(b.table())
diff --git a/index.d.ts b/index.d.ts
index fdf0cdb..25b9470 100644
--- a/index.d.ts
+++ b/index.d.ts
@@ -3,9 +3,11 @@
 export declare class DataFrame {
   len: number
   constructor(columns: Record<string, ColumnData>, len: number)
+  static fromColumns(columns: Record<string, ColumnData>): DataFrame
   colDtype(col: string): DataType | null
   get(col: string): ColumnData | null
   get columns(): Record<string, ColumnData>
+  writeCsv(path: string): void
 }
 
 export declare function asBooleanArray(column: ColumnData): Array<boolean> | null
@@ -30,3 +32,11 @@ export declare const enum DataType {
 }
 
 export declare function readCsv(path: string): DataFrame
+
+export declare function toBoolColumn(data: Array<boolean>): ColumnData
+
+export declare function toFloatColumn(data: Array<number>): ColumnData
+
+export declare function toIntColumn(data: Array<number>): ColumnData
+
+export declare function toStringColumn(data: Array<string>): ColumnData
diff --git a/index.js b/index.js
index 834c7fe..c53cb9c 100644
--- a/index.js
+++ b/index.js
@@ -583,3 +583,7 @@ module.exports.asIntArray = nativeBinding.asIntArray
 module.exports.asStringArray = nativeBinding.asStringArray
 module.exports.DataType = nativeBinding.DataType
 module.exports.readCsv = nativeBinding.readCsv
+module.exports.toBoolColumn = nativeBinding.toBoolColumn
+module.exports.toFloatColumn = nativeBinding.toFloatColumn
+module.exports.toIntColumn = nativeBinding.toIntColumn
+module.exports.toStringColumn = nativeBinding.toStringColumn
diff --git a/package.json b/package.json
index b9c794c..19b05b4 100644
--- a/package.json
+++ b/package.json
@@ -22,7 +22,6 @@
   ],
   "napi": {
     "binaryName": "sunbears",
-    "name": "sunbears",
     "targets": [
       "x86_64-pc-windows-msvc",
       "x86_64-apple-darwin",
@@ -71,6 +70,7 @@
     "ava": "^7.0.0",
     "chalk": "^5.6.2",
     "csv-parse": "^6.2.1",
+    "csv-stringify": "^6.7.0",
     "husky": "^9.1.7",
     "lint-staged": "^16.1.6",
     "npm-run-all2": "^8.0.4",
diff --git a/src/lib.rs b/src/lib.rs
index f34be11..3e7c43c 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -1,12 +1,14 @@
 #![deny(clippy::all)]
 
-use std::{collections::HashMap, fmt};
+use std::{collections::HashMap, fmt, fs::File, io::BufWriter};
 
 use anyhow::{anyhow, Result};
 
-use csv::Reader;
+use csv::{Reader, Writer};
 use napi_derive::napi;
 
+// ---------------------------------------- DATA MODELS ----------------------------------------
+
 #[napi]
 #[derive(Clone)]
 pub enum ColumnData {
@@ -16,6 +18,17 @@ pub enum ColumnData {
   Boolean(Vec<bool>),
 }
 
+impl ColumnData {
+  fn len(&self) -> usize {
+    match self {
+      Self::Boolean(b) => b.len(),
+      Self::Float(f) => f.len(),
+      Self::Integer(i) => i.len(),
+      Self::String(s) => s.len(),
+    }
+  }
+}
+
 #[napi]
 #[derive(PartialEq, Debug, Clone)]
 pub enum DataType {
@@ -37,6 +50,8 @@ impl fmt::Display for DataType {
   }
 }
 
+// ---------------------------------------- HELPER FUNCTIONS FOR DATA MODELS ----------------------------------------
+
 #[napi]
 pub fn as_float_array(column: ColumnData) -> Option<Vec<f64>> {
   match column {
@@ -69,6 +84,28 @@ pub fn as_string_array(column: ColumnData) -> Option<Vec<String>> {
   }
 }
 
+#[napi]
+pub fn to_string_column(data: Vec<String>) -> ColumnData {
+  ColumnData::String(data)
+}
+
+#[napi]
+pub fn to_float_column(data: Vec<f64>) -> ColumnData {
+  ColumnData::Float(data)
+}
+
+#[napi]
+pub fn to_int_column(data: Vec<i64>) -> ColumnData {
+  ColumnData::Integer(data)
+}
+
+#[napi]
+pub fn to_bool_column(data: Vec<bool>) -> ColumnData {
+  ColumnData::Boolean(data)
+}
+
+// ---------------------------------------- DATAFRAME ----------------------------------------
+
 #[napi(js_name = "DataFrame")]
 pub struct DataFrame {
   columns: HashMap<String, ColumnData>,
@@ -78,8 +115,26 @@ pub struct DataFrame {
 #[napi]
 impl DataFrame {
   #[napi(constructor)]
-  pub fn new(columns: HashMap<String, ColumnData>, len: u32) -> Self {
-    Self { columns, len }
+  pub fn new(columns: HashMap<String, ColumnData>, len: u32) -> Result<Self> {
+    if !columns.iter().all(|(_, c)| c.len() as u32 == len) {
+      return Err(anyhow!("Not all columns are of the declared length"));
+    }
+    Ok(Self { columns, len })
+  }
+
+  #[napi(factory)]
+  pub fn from_columns(columns: HashMap<String, ColumnData>) -> Result<Self> {
+    let keys: Vec<&String> = columns.keys().collect();
+    let col = &columns[keys[0]];
+    let l = col.len();
+    if !columns.iter().all(|(_, c)| c.len() == l) {
+      return Err(anyhow!("Not all columns are the same length"));
+    }
+
+    Ok(Self {
+      columns,
+      len: l as u32,
+    })
   }
 
   #[napi]
@@ -107,8 +162,52 @@ impl DataFrame {
   pub fn columns(&self) -> HashMap<String, ColumnData> {
     self.columns.clone()
   }
+
+  #[napi]
+  pub fn write_csv(&self, path: String) -> Result<()> {
+    let file = File::create(&path)?;
+    let buf = BufWriter::with_capacity(1 << 20, file); // 1 MB buffer size
+    let mut writer = Writer::from_writer(buf);
+    let mut float_buf = ryu::Buffer::new();
+    let mut int_buf = itoa::Buffer::new();
+    let keys: Vec<&String> = self.columns.keys().collect();
+    writer.write_record(keys.iter().map(|k| k.as_str()))?;
+    for i in 0..self.len as usize {
+      let mut row = Vec::with_capacity(keys.len());
+      for key in &keys {
+        let col = &self.columns[*key];
+        match col {
+          ColumnData::Boolean(b) => {
+            let v = if b[i] {
+              "true".to_string()
+            } else {
+              "false".to_string()
+            };
+            row.push(v);
+          }
+          ColumnData::Float(f) => {
+            let v = float_buf.format(f[i]).to_string();
+            row.push(v);
+          }
+          ColumnData::Integer(j) => {
+            let v = int_buf.format(j[i]).to_string();
+            row.push(v);
+          }
+          ColumnData::String(s) => {
+            row.push(s[i].clone());
+          }
+        }
+      }
+      writer.write_record(&row)?;
+    }
+    writer.flush()?;
+
+    Ok(())
+  }
 }
 
+// ---------------------------------------- CSV READING ----------------------------------------
+
 fn infer_dtype(s: &str) -> DataType {
   if s.eq_ignore_ascii_case("true") || s.eq_ignore_ascii_case("false") {
     return DataType::Boolean;
@@ -166,24 +265,35 @@ pub fn read_csv(path: String) -> Result<DataFrame> {
     let vc = &col_to_vec[*c];
     let data: ColumnData = match d {
       DataType::Boolean => {
-        let v = vc
-          .iter()
-          .enumerate()
-          .map(|(i, s)| {
-            str_to_bool(s).unwrap_or_else(|| panic!("Expecting bool at line {:?} for col {}", i, c))
-          })
-          .collect();
+        let mut v: Vec<bool> = vec![];
+        for el in vc {
+          match str_to_bool(el) {
+            Some(val) => v.push(val),
+            None => {
+              return Err(anyhow!(
+                "Expecting bool at line {:?} for col {}",
+                i + 1,
+                col_idx[*c]
+              ))
+            }
+          }
+        }
         ColumnData::Boolean(v)
       }
       DataType::Float => {
-        let v = vc
-          .iter()
-          .enumerate()
-          .map(|(i, s)| {
-            s.parse::<f64>()
-              .unwrap_or_else(|_| panic!("Expecting float at line {:?} for col {}", i, c))
-          })
-          .collect();
+        let mut v: Vec<f64> = vec![];
+        for s in vc {
+          match s.parse::<f64>() {
+            Ok(val) => v.push(val),
+            Err(_) => {
+              return Err(anyhow!(
+                "Expecting float at line {:?} for col {}",
+                i + 1,
+                col_idx[*c]
+              ))
+            }
+          }
+        }
 
         ColumnData::Float(v)
       }
@@ -192,14 +302,19 @@ pub fn read_csv(path: String) -> Result<DataFrame> {
         ColumnData::String(v)
       }
       DataType::Integer => {
-        let v = vc
-          .iter()
-          .enumerate()
-          .map(|(i, s)| {
-            s.parse::<i64>()
-              .unwrap_or_else(|_| panic!("Expecting integer at line {:?} for col {}", i, c))
-          })
-          .collect();
+        let mut v: Vec<i64> = vec![];
+        for s in vc {
+          match s.parse::<i64>() {
+            Ok(val) => v.push(val),
+            Err(_) => {
+              return Err(anyhow!(
+                "Expecting integer at line {:?} for col {}",
+                i + 1,
+                col_idx[*c]
+              ))
+            }
+          }
+        }
 
         ColumnData::Integer(v)
       }
@@ -207,7 +322,7 @@ pub fn read_csv(path: String) -> Result<DataFrame> {
     cols.insert(col_idx[*c].to_owned(), data);
   }
 
-  let df = DataFrame::new(cols, i);
+  let df = DataFrame::new(cols, i)?;
 
   Ok(df)
 }
diff --git a/testfiles/comparative_bench.py b/testfiles/comparative_bench.py
index 00ceed6..4498365 100755
--- a/testfiles/comparative_bench.py
+++ b/testfiles/comparative_bench.py
@@ -6,12 +6,36 @@
 # ///
 
 
+import os
 import timeit
+from glob import glob
 
 import pandas as pd
 import polars as pl
 
 
+def generate_df_with_n_rows(n: int, pandas: bool) -> pl.DataFrame | pd.DataFrame:
+    data = {
+        "col1": ["something"] * n,
+        "col2": [1.0] * n,
+        "col3": [3] * n,
+        "col4": [True] * n,
+    }
+    if pandas:
+        return pd.DataFrame(data)
+    return pl.DataFrame(data)
+
+
+pl_100_df = generate_df_with_n_rows(100, False)
+pl_1000_df = generate_df_with_n_rows(1000, False)
+pl_100000_df = generate_df_with_n_rows(100000, False)
+pl_1000000_df = generate_df_with_n_rows(1000000, False)
+pd_100_df = generate_df_with_n_rows(100, True)
+pd_1000_df = generate_df_with_n_rows(1000, True)
+pd_100000_df = generate_df_with_n_rows(100000, True)
+pd_1000000_df = generate_df_with_n_rows(1000000, True)
+
+
 def read_100_with_polars() -> None:
     """
     Read a 100-rows CSV with Polars.
@@ -69,6 +93,44 @@ def read_1000000_with_pandas() -> None:
     pd.read_csv("testfiles/generated-1000000.csv")
 
 
+def write_100_with_pandas() -> None:
+    pd_100_df.to_csv("testfiles/pd-100.csv", index=False)
+
+
+def write_1000_with_pandas() -> None:
+    pd_1000_df.to_csv("testfiles/pd-1000.csv", index=False)
+
+
+def write_100000_with_pandas() -> None:
+    pd_100000_df.to_csv("testfiles/pd-100000.csv", index=False)
+
+
+def write_1000000_with_pandas() -> None:
+    pd_1000000_df.to_csv("testfiles/pd-1000000.csv", index=False)
+
+
+def write_100_with_polars() -> None:
+    pl_100_df.write_csv("testfiles/pl-100.csv")
+
+
+def write_1000_with_polars() -> None:
+    pl_1000_df.write_csv("testfiles/pl-1000.csv")
+
+
+def write_100000_with_polars() -> None:
+    pl_100000_df.write_csv("testfiles/pl-100000.csv")
+
+
+def write_1000000_with_polars() -> None:
+    pl_1000000_df.write_csv("testfiles/pl-1000000.csv")
+
+
+def cleanup() -> None:
+    files = glob("testfiles/p?-1*.csv")
+    for file in files:
+        os.remove(file)
+
+
 def main() -> None:
     t100_pd = timeit.timeit(read_100_with_pandas, number=200)
     t1000_pd = timeit.timeit(read_1000_with_pandas, number=100)
@@ -79,11 +141,24 @@ def main() -> None:
     t100000_pl = timeit.timeit(read_100000_with_polars, number=20)
     t1000000_pl = timeit.timeit(read_1000000_with_polars, number=20)
 
+    wt100_pd = timeit.timeit(write_100_with_pandas, number=200)
+    wt1000_pd = timeit.timeit(write_1000_with_pandas, number=100)
+    wt100000_pd = timeit.timeit(write_100000_with_pandas, number=20)
+    wt1000000_pd = timeit.timeit(write_1000000_with_pandas, number=20)
+    wt100_pl = timeit.timeit(write_100_with_polars, number=200)
+    wt1000_pl = timeit.timeit(write_1000_with_polars, number=100)
+    wt100000_pl = timeit.timeit(write_100000_with_polars, number=20)
+    wt1000000_pl = timeit.timeit(write_1000000_with_polars, number=20)
+
     rows = [
-        ("100 lines", t100_pd, t100_pl),
-        ("1000 lines", t1000_pd, t1000_pl),
-        ("100000 lines", t100000_pd, t100000_pl),
-        ("1000000 lines", t1000000_pd, t1000000_pl),
+        ("Read 100 lines", t100_pd, t100_pl),
+        ("Read 1000 lines", t1000_pd, t1000_pl),
+        ("Read 100000 lines", t100000_pd, t100000_pl),
+        ("Read 1000000 lines", t1000000_pd, t1000000_pl),
+        ("Write 100 lines", wt100_pd, wt100_pl),
+        ("Write 1000 lines", wt1000_pd, wt1000_pl),
+        ("Write 100000 lines", wt100000_pd, wt100000_pl),
+        ("Write 1000000 lines", wt1000000_pd, wt1000000_pl),
     ]
 
     col0, col1, col2 = "Dataset", "Pandas (s)", "Polars (s)"
@@ -101,6 +176,8 @@ def main() -> None:
         print(f"| {name:<{w0}} | {pd_t:<{w1}.6f} | {pl_t:<{w2}.6f} |")
     print(sep)
 
+    cleanup()
+
 
 if __name__ == "__main__":
     main()
diff --git a/testfiles/err-typed-col-1.csv b/testfiles/err-typed-col-1.csv
new file mode 100644
index 0000000..8909933
--- /dev/null
+++ b/testfiles/err-typed-col-1.csv
@@ -0,0 +1,3 @@
+col1,col2,colErr
+1,true,1
+2,false,2.3
diff --git a/testfiles/err-typed-col-2.csv b/testfiles/err-typed-col-2.csv
new file mode 100644
index 0000000..ecebe36
--- /dev/null
+++ b/testfiles/err-typed-col-2.csv
@@ -0,0 +1,3 @@
+col1,col2,colErr
+1,true,12345678
+2,false,+1 345-6-78
diff --git a/testfiles/generated-100.csv b/testfiles/generated-100.csv
index a91b020..ea61a40 100644
--- a/testfiles/generated-100.csv
+++ b/testfiles/generated-100.csv
@@ -1,101 +1,101 @@
 name,age,is_working,height,phone_number,years_of_experience
-Enid,45,false,151.5466823228966,0043-1234,2.5778831651313947
-Anne,27,false,157.32057957197924,0043-1234,3.920121676134699
-Anne,33,false,172.136358978696,0043-1234,0.8587354612575759
-Charles,37,false,171.33244976651685,0043-1234,2.903354645818903
-Anne,34,true,174.21281615745679,0043-1234,3.152821943088975
-Charles,24,false,161.73810910236352,0043-1234,1.24912967135455
-Enid,26,false,163.20241491839687,0043-1234,1.3843500318447932
-Enid,50,true,174.58942179951632,0043-1234,0.29440806510794815
-Charles,40,true,160.32882761029387,+1 1234,4.750086123138027
-Francis,20,false,177.84463400681972,0043-1234,3.590579920066384
-Anne,28,false,180.7559581929724,0043-1234,3.8079258128713915
-Enid,43,true,162.2578081838651,+1 1234,3.943149128054852
-Francis,45,true,161.15792835686182,+1 1234,3.275121349491939
-Charles,28,true,173.52374548303726,0043-1234,2.2907905374973434
-Charles,27,false,167.53436253284644,+1 1234,3.036343574334585
-Francis,41,false,151.6681706051119,+1 1234,4.704139386820414
-Charles,43,true,158.46248923827525,+1 1234,3.6361156343617385
-Charles,43,true,176.16091128359244,+1 1234,4.526527468976222
-Francis,29,false,171.5240388288034,+1 1234,1.3341152606202553
-Dylan,36,false,177.03758652443665,+1 1234,3.5017422747629494
-Dylan,45,true,168.21283391386254,0043-1234,2.1148839392310013
-Enid,34,true,152.4336293492877,0043-1234,4.938747197130386
-Charles,37,true,158.39485545404779,0043-1234,2.076732739300934
-Bob,36,true,158.75970242956387,0043-1234,2.9789814987774665
-Francis,44,false,177.36154981808326,0043-1234,4.197012112866403
-Francis,21,true,163.58058955398462,0043-1234,0.1290727333852506
-Charles,23,true,176.4620090811987,+1 1234,1.9565531871692177
-Francis,46,true,173.67591891696253,0043-1234,3.475410636322101
-Francis,43,false,174.1497585797949,+1 1234,2.4334043109864156
-Dylan,35,false,163.3179439068544,+1 1234,4.456175047352048
-Bob,26,false,156.89588160262397,0043-1234,1.7440901131079198
-Bob,25,true,174.02640553149135,+1 1234,2.96007370677206
-Dylan,27,false,163.06869544510883,0043-1234,1.1350907683849853
-Francis,36,true,163.92732636598666,0043-1234,3.837165706004641
-Dylan,22,false,169.22480371321228,0043-1234,3.241646789199385
-Dylan,39,false,159.94975264421598,0043-1234,1.4793931230161022
-Enid,27,true,180.63096161699352,0043-1234,1.731196161419024
-Bob,31,true,180.27010334955744,0043-1234,3.3868422932758686
-Bob,38,false,166.5457493309321,0043-1234,2.5500213541433325
-Francis,37,true,159.4830413538787,0043-1234,1.0761979352612872
-Francis,45,false,150.8110611015065,+1 1234,3.766570934362565
-Enid,29,true,159.5999294435609,+1 1234,3.444842149699685
-Francis,44,true,179.9218787546004,0043-1234,1.0536416152316959
-Dylan,32,true,159.9604202358432,0043-1234,3.6030971831385328
-Francis,45,false,172.2728449812043,+1 1234,3.1972307584450004
-Dylan,23,true,178.38682904606802,+1 1234,1.1139663330328458
-Dylan,43,true,165.91595703550433,0043-1234,1.663690739056932
-Charles,37,true,164.97907400959514,+1 1234,1.059942097552784
-Francis,21,true,152.51438354447447,+1 1234,3.1689985168144226
-Francis,27,true,163.12175423186218,0043-1234,3.8489905588458333
-Charles,25,false,178.02960891566104,0043-1234,4.6216027506403865
-Enid,23,false,166.83457465997702,0043-1234,3.8774566196190325
-Francis,31,false,165.31805063687807,+1 1234,3.4656451539345836
-Bob,43,false,168.21950087249613,+1 1234,1.0603604183311441
-Charles,32,false,177.4090685505179,0043-1234,4.604355730257678
-Enid,34,false,178.72611537672864,0043-1234,1.5270268273472656
-Anne,29,false,166.57616492581164,+1 1234,0.8760047145100074
-Anne,22,false,165.40220309671685,0043-1234,4.8832531600674445
-Francis,46,true,167.74924727046664,0043-1234,1.868036709110598
-Francis,39,false,171.34656089350054,0043-1234,2.839303404514995
-Anne,43,false,171.70729294284888,+1 1234,1.026264394218072
-Enid,27,true,170.97086309150654,0043-1234,2.4077883659503927
-Anne,21,true,155.54580394718937,0043-1234,1.9208241110559379
-Bob,36,true,180.10669124042226,+1 1234,1.9623607711430755
-Anne,21,false,150.30057047559959,0043-1234,1.5292597422560075
-Enid,42,false,155.3581224019092,0043-1234,3.2138244136599496
-Dylan,43,false,167.56206691233936,0043-1234,0.8422819556789753
-Bob,35,false,162.86264479579643,+1 1234,0.5569161225719821
-Enid,33,true,154.0846693644162,+1 1234,1.4359544202248402
-Bob,41,true,172.57865473074597,+1 1234,2.055424188216807
-Enid,26,false,152.15799419687647,+1 1234,4.719488876086064
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+Charles,39,false,177.19248309034617,0043-1234,0.7043319312506218
+Dylan,27,false,163.78268872264073,0043-1234,0.7933675094289101
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+Anne,24,true,167.0624790612009,0043-1234,3.208757157964802
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+Enid,25,true,165.3165808966044,0043-1234,2.907435266125858
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+Dylan,21,true,180.24598488538015,+1 1234,2.4544469347421005
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+Dylan,30,true,157.385569900398,0043-1234,4.456103378715245
+Enid,24,false,151.93522138656664,0043-1234,3.456877765620685
+Charles,31,false,178.33999739259212,+1 1234,4.708568405839408
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diff --git a/tsconfig.json b/tsconfig.json
index 5056788..df7d9d9 100644
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -7,7 +7,8 @@
     "noUnusedLocals": true,
     "noUnusedParameters": true,
     "esModuleInterop": true,
-    "allowSyntheticDefaultImports": true
+    "allowSyntheticDefaultImports": true,
+    "types": ["node"]
   },
   "include": ["."],
   "exclude": ["node_modules", "bench", "__test__"]
diff --git a/yarn.lock b/yarn.lock
index 301f211..949950c 100644
--- a/yarn.lock
+++ b/yarn.lock
@@ -19,6 +19,7 @@ __metadata:
     ava: "npm:^7.0.0"
     chalk: "npm:^5.6.2"
     csv-parse: "npm:^6.2.1"
+    csv-stringify: "npm:^6.7.0"
     husky: "npm:^9.1.7"
     lint-staged: "npm:^16.1.6"
     npm-run-all2: "npm:^8.0.4"
@@ -1840,6 +1841,13 @@ __metadata:
   languageName: node
   linkType: hard
 
+"csv-stringify@npm:^6.7.0":
+  version: 6.7.0
+  resolution: "csv-stringify@npm:6.7.0"
+  checksum: 10c0/2f1272e0462c31935650b42f34cb5dafbbc9a3b479074283ec4ed6491756c791d2d6230226c530198d85002d4d3f3b69da14b1f424ab93ad3263124ea25e5ea3
+  languageName: node
+  linkType: hard
+
 "currently-unhandled@npm:^0.4.1":
   version: 0.4.1
   resolution: "currently-unhandled@npm:0.4.1"