{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas DataFrame with N/As\n", "\n", "Solving GitHub [issue number 3](https://github.com/ideonate/nb2xls/issues/3)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
0
0NaN
\n", "
" ], "text/plain": [ " 0\n", "0 NaN" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame([[pd.np.nan]])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
col1
01.0
1NaN
22.5
3NaN
\n", "
" ], "text/plain": [ " col1\n", "0 1.0\n", "1 NaN\n", "2 2.5\n", "3 NaN" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame({'col1': [1.0, pd.np.nan, 2.5, float('nan')]})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }