Before nbqa

before.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38663d6b-3b71-4809-b624-d0651c3dc6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e749ae38-f05f-453d-b067-3b773fa1251d",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1 +     1\n",
    "b = a + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f312ae3-a96d-4750-a0c8-c1536b887e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "012a6363-a57c-4191-8044-1b5e10c228a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,4])\n",
    "x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e40bfcd-716c-43bb-82e8-679312aab136",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f3a2a1c-5d85-43e6-8d46-3e424ded70ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame({\"colname\": x})"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}