Python-100-Days/Day66-70/code/Day68.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = 'FZJKai-Z03S'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "一季度    320\n",
       "二季度    180\n",
       "三季度    300\n",
       "四季度    405\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series(data=[320, 180, 300, 405], index=['一季度', '二季度', '三季度', '四季度'])\n",
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "一季度    320\n",
       "二季度    180\n",
       "三季度    300\n",
       "四季度    405\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2 = pd.Series({'一季度': 320, '二季度': 180, '三季度': 300, '四季度': 405})\n",
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "320 300 405\n",
      "一季度    350\n",
      "二季度    180\n",
      "三季度    300\n",
      "四季度    360\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ser2[0], ser2[2], ser2[-1])\n",
    "ser2[0], ser2[-1] = 350, 360 \n",
    "print(ser2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350 300\n",
      "一季度    380\n",
      "二季度    180\n",
      "三季度    300\n",
      "四季度    360\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ser2['一季度'], ser2['三季度'])\n",
    "ser2['一季度'] = 380\n",
    "print(ser2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二季度    180\n",
      "三季度    300\n",
      "dtype: int64\n",
      "二季度    180\n",
      "三季度    300\n",
      "四季度    360\n",
      "dtype: int64\n",
      "一季度    380\n",
      "二季度    400\n",
      "三季度    500\n",
      "四季度    360\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ser2[1:3])\n",
    "print(ser2['二季度': '四季度'])\n",
    "ser2[1:3] = 400, 500\n",
    "print(ser2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二季度    400\n",
      "四季度    360\n",
      "dtype: int64\n",
      "一季度    380\n",
      "二季度    500\n",
      "三季度    500\n",
      "四季度    520\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ser2[['二季度', '四季度']])\n",
    "ser2[['二季度', '四季度']] = 500, 520\n",
    "print(ser2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二季度    500\n",
      "三季度    500\n",
      "四季度    520\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ser2[ser2 >= 500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1900\n",
      "475.0\n",
      "520\n",
      "380\n",
      "4\n",
      "64.03124237432849\n",
      "4100.0\n",
      "500.0\n"
     ]
    }
   ],
   "source": [
    "# 求和\n",
    "print(ser2.sum())\n",
    "# 求均值\n",
    "print(ser2.mean())\n",
    "# 求最大\n",
    "print(ser2.max())\n",
    "# 求最小\n",
    "print(ser2.min())\n",
    "# 计数\n",
    "print(ser2.count())\n",
    "# 求标准差\n",
    "print(ser2.std())\n",
    "# 求方差\n",
    "print(ser2.var())\n",
    "# 求中位数\n",
    "print(ser2.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      4.000000\n",
       "mean     475.000000\n",
       "std       64.031242\n",
       "min      380.000000\n",
       "25%      470.000000\n",
       "50%      500.000000\n",
       "75%      505.000000\n",
       "max      520.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     3\n",
       "pitaya    2\n",
       "durian    1\n",
       "banana    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser3 = pd.Series(data=['apple', 'banana', 'apple', 'pitaya', 'apple', 'pitaya', 'durian'])\n",
    "ser3.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1    20.0\n",
       "3    30.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser4 = pd.Series(data=[10, 20, np.NaN, 30, np.NaN])\n",
    "ser4.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1    20.0\n",
       "2    40.0\n",
       "3    30.0\n",
       "4    40.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser4.fillna(value=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1    20.0\n",
       "2    20.0\n",
       "3    30.0\n",
       "4    30.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser4.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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