{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Examples**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']\n", "data = dict([(t, np.ones(shape=3000).astype(t))\n", " for t in dtypes])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | int64 | \n", "float64 | \n", "complex128 | \n", "object | \n", "bool | \n", "
---|---|---|---|---|---|
0 | \n", "1 | \n", "1.0 | \n", "(1+0j) | \n", "1 | \n", "True | \n", "
1 | \n", "1 | \n", "1.0 | \n", "(1+0j) | \n", "1 | \n", "True | \n", "
2 | \n", "1 | \n", "1.0 | \n", "(1+0j) | \n", "1 | \n", "True | \n", "
3 | \n", "1 | \n", "1.0 | \n", "(1+0j) | \n", "1 | \n", "True | \n", "
4 | \n", "1 | \n", "1.0 | \n", "(1+0j) | \n", "1 | \n", "True | \n", "