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imports update for pandas namespace
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‎maria/Intro Turorial.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0e956d07",
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"metadata": {},
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"source": [
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"## Quick start"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c4f4ecab",
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"metadata": {},
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"source": [
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"https://narwhals-dev.github.io/narwhals/installation/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a49c9620",
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import annotations\n",
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"import pandas as pd\n",
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"import polars as pl\n",
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"import pyarrow as pa\n",
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"import narwhals as nw\n",
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"from narwhals.typing import IntoFrame"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1aa0f584",
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"metadata": {},
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"outputs": [],
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"source": [
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"def agnostic_get_columns(df_native: IntoFrame) -> list[str]:\n",
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" df = nw.from_native(df_native)\n",
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" column_names = df.columns\n",
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" return column_names"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "7dc54474",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = {\"a\": [1, 2, 3], \"b\": [4, 5, 6]}\n",
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"df_pandas = pd.DataFrame(data)\n",
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"df_polars = pl.DataFrame(data)\n",
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"table_pa = pa.table(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "89939c27",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pandas output\n",
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"['a', 'b']\n",
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"Polars output\n",
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"['a', 'b']\n",
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"PyArrow output\n",
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"['a', 'b']\n"
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]
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}
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],
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"source": [
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"print(\"pandas output\")\n",
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"print(agnostic_get_columns(df_pandas))\n",
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"\n",
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"print(\"Polars output\")\n",
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"print(agnostic_get_columns(df_polars))\n",
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"\n",
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"print(\"PyArrow output\")\n",
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"print(agnostic_get_columns(table_pa))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1c108e21",
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"metadata": {},
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"source": [
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"This is the simplest possible example of a dataframe-agnostic function - as we'll soon see, we can do much more advanced things."
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]
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},
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{
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"cell_type": "markdown",
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"id": "839e22c4",
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"metadata": {},
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"source": [
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"## DataFrame"
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]
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},
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{
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"cell_type": "markdown",
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"id": "36af6dfd",
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"metadata": {},
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"source": [
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"To write a dataframe-agnostic function, the steps you'll want to follow are:\n",
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"\n",
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"1. Initialise a Narwhals DataFrame or LazyFrame by passing your dataframe to `nw.from_native`. All the calculations stay lazy if we start with a lazy dataframe - Narwhals will never automatically trigger computation without you asking it to.\n",
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"\n",
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" Note: if you need eager execution, make sure to pass `eager_only=True` to `nw.from_native`.\n",
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"\n",
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"2. Express your logic using the subset of the Polars API supported by Narwhals. \n",
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"\n",
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"3. If you need to return a dataframe to the user in its original library, call `nw.to_native`.\n",
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"\n",
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"Steps 1 and 3 are so common that we provide a utility `@nw.narwhalify` decorator, which allows you to only explicitly write step 2.\n",
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"\n",
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"Let's explore this with some simple examples.\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e8003bf9",
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"metadata": {},
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"source": [
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"### Example 1: descriptive statistics"
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]
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},
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{
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"cell_type": "markdown",
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"id": "36b782d0",
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"metadata": {},
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"source": [
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"Just like in Polars, we can pass expressions to DataFrame.select or LazyFrame.select."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "52fbbb9c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from narwhals.typing import IntoFrameT\n",
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"\n",
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"def func(df: IntoFrameT) -> IntoFrameT:\n",
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" return (\n",
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" nw.from_native(df)\n",
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" .select(\n",
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" a_sum=nw.col(\"a\").sum(),\n",
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" a_mean=nw.col(\"a\").mean(),\n",
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" a_std=nw.col(\"a\").std(),\n",
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" )\n",
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" .to_native()\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "98996849",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" a_sum a_mean a_std\n",
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"0 4 1.333333 0.57735\n"
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]
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}
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],
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"source": [
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"# check in pandas\n",
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"df = pd.DataFrame({\"a\":[1,1,2]})\n",
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"print(func(df))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aca15172",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"shape: (1, 3)\n",
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"┌───────┬──────────┬─────────┐\n",
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"│ a_sum ┆ a_mean ┆ a_std │\n",
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"│ --- ┆ --- ┆ --- │\n",
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"│ i64 ┆ f64 ┆ f64 │\n",
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"╞═══════╪══════════╪═════════╡\n",
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"│ 4 ┆ 1.333333 ┆ 0.57735 │\n",
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"└───────┴──────────┴─────────┘\n"
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]
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}
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],
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"source": [
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"# check in polars\n",
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"df = pl.DataFrame({\"a\": [1,1,2]})\n",
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"print(func(df))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b192cb5a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pyarrow.Table\n",
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"a_sum: int64\n",
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"a_mean: double\n",
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"a_std: double\n",
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"----\n",
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"a_sum: [[4]]\n",
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"a_mean: [[1.3333333333333333]]\n",
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"a_std: [[0.5773502691896257]]\n"
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]
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}
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],
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"source": [
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"# check in PyArrow\n",
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"table = pa.table({\"a\": [1,1,2]})\n",
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"print(func(table))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e116914e",
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"metadata": {},
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"source": [
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"### Example 2: group-by and mean\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4f33ced7",
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"metadata": {},
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"source": [
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"Just like in Polars, we can pass expressions to GroupBy.agg. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "4ca29c0d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def func(df: IntoFrameT) -> IntoFrameT:\n",
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" return(\n",
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" nw.from_native(df).group_by(\"a\").agg(nw.col(\"b\").mean()).sort(\"a\").to_native()\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d701eefd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" a b\n",
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"0 1 4.5\n",
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"1 2 6.0\n"
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]
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}
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],
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"source": [
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"# check in pandas\n",
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"df = pd.DataFrame({\"a\": [1, 1, 2], \"b\": [4, 5, 6]})\n",
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"print(func(df))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0d9430f0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"shape: (2, 2)\n",
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"┌─────┬─────┐\n",
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"│ a ┆ b │\n",
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"│ --- ┆ --- │\n",
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"│ i64 ┆ f64 │\n",
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"╞═════╪═════╡\n",
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"│ 1 ┆ 4.5 │\n",
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"│ 2 ┆ 6.0 │\n",
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"└─────┴─────┘\n"
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]
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}
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],
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"source": [
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"# check in polars\n",
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"df = pl.DataFrame({\"a\": [1, 1, 2], \"b\": [4, 5, 6]})\n",
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"print(func(df))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "a26c46bb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pyarrow.Table\n",
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"a: int64\n",
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"b: double\n",
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"----\n",
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"a: [[1,2]]\n",
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"b: [[4.5,6]]\n"
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]
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}
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],
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"source": [
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"#check in PyArrow\n",
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"table = pa.table({\"a\": [1, 1, 2], \"b\": [4, 5, 6]})\n",
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"print(func(table))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9ee2857b",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "narwhals",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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