1064 lines
18 KiB
Plaintext
1064 lines
18 KiB
Plaintext
{
|
||
"cells": [
|
||
{
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||
"cell_type": "markdown",
|
||
"metadata": {
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||
"collapsed": false,
|
||
"pycharm": {
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||
"name": "#%% md\n"
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||
}
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||
},
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||
"source": [
|
||
"# 什么是 Series？"
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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": 1,
|
||
"metadata": {
|
||
"collapsed": true
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||
},
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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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"0 1\n",
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"1 2\n",
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"2 3\n",
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"3 4\n",
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"4 5\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"data = [1, 2, 3, 4, 5]\n",
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"series = pd.Series(data)\n",
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"print(series)"
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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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||
"metadata": {
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||
"collapsed": false,
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||
"pycharm": {
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||
"name": "#%%\n"
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||
}
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||
},
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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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"RangeIndex(start=0, stop=5, step=1)\n"
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||
]
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}
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],
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"source": [
|
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"print(series.index)"
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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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||
"metadata": {
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||
"collapsed": false,
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||
"pycharm": {
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||
"name": "#%%\n"
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||
}
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||
},
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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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"count 5.000000\n",
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"mean 3.000000\n",
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"std 1.581139\n",
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"min 1.000000\n",
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"25% 2.000000\n",
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||
"50% 3.000000\n",
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"75% 4.000000\n",
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||
"max 5.000000\n",
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"dtype: float64\n"
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]
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}
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],
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"source": [
|
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"print(series.describe())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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||
"collapsed": false,
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||
"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# 常用的 Series 属性"
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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": 4,
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||
"metadata": {
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||
"collapsed": false,
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||
"pycharm": {
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||
"name": "#%%\n"
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||
}
|
||
},
|
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"outputs": [
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{
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||
"data": {
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||
"text/plain": [
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"0 44\n",
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"1 41\n",
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"2 12\n",
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"3 34\n",
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"4 15\n",
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"Name: numbers, dtype: int64"
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||
]
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||
},
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||
"execution_count": 4,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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||
],
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"source": [
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"import random\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"random.seed(233)\n",
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"\n",
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"data = [random.randrange(1, 50) for _ in range(100)]\n",
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"series = pd.Series(data, name=\"numbers\")\n",
|
||
"series.head(5)"
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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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||
"metadata": {
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||
"collapsed": false,
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||
"pycharm": {
|
||
"name": "#%%\n"
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||
}
|
||
},
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||
"outputs": [
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||
{
|
||
"data": {
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||
"text/plain": [
|
||
"RangeIndex(start=0, stop=100, step=1)"
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||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
|
||
],
|
||
"source": [
|
||
"series.index"
|
||
]
|
||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
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||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
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||
"name": "#%%\n"
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||
}
|
||
},
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||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n"
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||
]
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||
}
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||
],
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||
"source": [
|
||
"print(list(series.index))"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 7,
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||
"metadata": {
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||
"collapsed": false,
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||
"pycharm": {
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||
"name": "#%%\n"
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||
}
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||
},
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||
"outputs": [
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||
{
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||
"data": {
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||
"text/plain": [
|
||
"dtype('int64')"
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||
]
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||
},
|
||
"execution_count": 7,
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||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
|
||
],
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||
"source": [
|
||
"series.dtype"
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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,
|
||
"metadata": {
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||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(100,)"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"100"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.size"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'numbers'"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.name"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([44, 41, 12, 34, 15, 31, 36, 14, 6, 7, 18, 15, 45, 46, 1, 5, 44,\n",
|
||
" 16, 39, 46, 46, 15, 48, 19, 8, 31, 21, 14, 6, 19, 32, 34, 41, 10,\n",
|
||
" 40, 46, 22, 20, 7, 33, 29, 48, 18, 27, 3, 26, 48, 12, 4, 49, 5,\n",
|
||
" 49, 8, 14, 11, 23, 21, 48, 34, 34, 29, 9, 2, 30, 8, 45, 23, 46,\n",
|
||
" 43, 7, 45, 6, 37, 24, 4, 26, 20, 35, 3, 39, 33, 30, 26, 47, 1,\n",
|
||
" 5, 31, 23, 25, 9, 41, 29, 23, 20, 39, 48, 47, 9, 14, 32])"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%% md\n"
|
||
}
|
||
},
|
||
"source": [
|
||
"# 常用的 Series 方法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"arr = [random.randrange(1, 50) for _ in range(100)]\n",
|
||
"arr = pd.Series(arr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 数学运算 / 统计"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 79\n",
|
||
"1 85\n",
|
||
"2 21\n",
|
||
"3 60\n",
|
||
"4 60\n",
|
||
" ..\n",
|
||
"95 55\n",
|
||
"96 93\n",
|
||
"97 25\n",
|
||
"98 51\n",
|
||
"99 48\n",
|
||
"Length: 100, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.add(arr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 9\n",
|
||
"1 -3\n",
|
||
"2 3\n",
|
||
"3 8\n",
|
||
"4 -30\n",
|
||
" ..\n",
|
||
"95 41\n",
|
||
"96 1\n",
|
||
"97 -7\n",
|
||
"98 -23\n",
|
||
"99 16\n",
|
||
"Length: 100, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.sub(arr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 1540\n",
|
||
"1 1804\n",
|
||
"2 108\n",
|
||
"3 884\n",
|
||
"4 675\n",
|
||
" ... \n",
|
||
"95 336\n",
|
||
"96 2162\n",
|
||
"97 144\n",
|
||
"98 518\n",
|
||
"99 512\n",
|
||
"Length: 100, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.mul(arr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 1.257143\n",
|
||
"1 0.931818\n",
|
||
"2 1.333333\n",
|
||
"3 1.307692\n",
|
||
"4 0.333333\n",
|
||
" ... \n",
|
||
"95 6.857143\n",
|
||
"96 1.021739\n",
|
||
"97 0.562500\n",
|
||
"98 0.378378\n",
|
||
"99 2.000000\n",
|
||
"Length: 100, dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.div(arr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"49"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.max()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"1"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.min()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"25.5"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.median()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"25.51"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"14.990566056926294"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series.std()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 函数应用"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 True\n",
|
||
"1 False\n",
|
||
"2 True\n",
|
||
"3 True\n",
|
||
"4 False\n",
|
||
"Name: numbers, dtype: bool"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"def mod2(x):\n",
|
||
" if x % 2 == 0:\n",
|
||
" return True\n",
|
||
" return False\n",
|
||
"\n",
|
||
"\n",
|
||
"idx = series.map(mod2)\n",
|
||
"idx.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 44\n",
|
||
"2 12\n",
|
||
"3 34\n",
|
||
"6 36\n",
|
||
"7 14\n",
|
||
"Name: numbers, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series[idx].head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 True\n",
|
||
"1 False\n",
|
||
"2 True\n",
|
||
"3 True\n",
|
||
"4 False\n",
|
||
"Name: numbers, dtype: bool"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"idx = series.apply(mod2)\n",
|
||
"idx.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 44\n",
|
||
"2 12\n",
|
||
"3 34\n",
|
||
"6 36\n",
|
||
"7 14\n",
|
||
"Name: numbers, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"series[idx].head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%% md\n"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 特定类型接口"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"str_series = pd.Series([\"apple\", \"orange\", \"banana\", \"grape\"], name=\"fruit\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 1\n",
|
||
"1 1\n",
|
||
"2 3\n",
|
||
"3 1\n",
|
||
"Name: fruit, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"str_series.str.count(\"a\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 5\n",
|
||
"1 6\n",
|
||
"2 6\n",
|
||
"3 5\n",
|
||
"Name: fruit, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"str_series.str.len()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/ipykernel_83159/772644344.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
|
||
" str_series.str.replace(\"[p]{2}\", \"XX\")\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 aXXle\n",
|
||
"1 orange\n",
|
||
"2 banana\n",
|
||
"3 grape\n",
|
||
"Name: fruit, dtype: object"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"str_series.str.replace(\"[p]{2}\", \"XX\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"dt_series = pd.Series([20220101, 20220201, 20220301, 20220401], name=\"date\").astype(\"string\")\n",
|
||
"dt_series = pd.to_datetime(dt_series, format=\"%Y-%m-%d\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"dtype('<M8[ns]')"
|
||
]
|
||
},
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dt_series.dtype"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 2022-01-01\n",
|
||
"1 2022-02-01\n",
|
||
"2 2022-03-01\n",
|
||
"3 2022-04-01\n",
|
||
"Name: date, dtype: datetime64[ns]"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dt_series.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 1\n",
|
||
"1 2\n",
|
||
"2 3\n",
|
||
"3 4\n",
|
||
"Name: date, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dt_series.dt.month"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 1\n",
|
||
"1 32\n",
|
||
"2 60\n",
|
||
"3 91\n",
|
||
"Name: date, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dt_series.dt.dayofyear"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 5\n",
|
||
"1 1\n",
|
||
"2 1\n",
|
||
"3 4\n",
|
||
"Name: date, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dt_series.dt.weekday"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3.10.4 ('sspai-100-hours-series-python')",
|
||
"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": "3.10.4"
|
||
},
|
||
"vscode": {
|
||
"interpreter": {
|
||
"hash": "7a101baf08afe636412f97dd4a9fc2e65b6f84f0ec50413bf3e19b04a26b8ba6"
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|