Python-100-Days/Day66-80/code/Day70.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ],
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   "metadata": {}
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  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='svg'"
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   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.rcParams['font.sans-serif'] = 'FZJKai-Z03S'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
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   "cell_type": "code",
   "execution_count": null,
   "source": [
    "ser1 = pd.Series(data=[320, 180, 300, 405], index=['一季度', '二季度', '三季度', '四季度'])\n",
    "ser1"
   ],
   "outputs": [],
   "metadata": {}
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  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "ser2 = pd.Series({'一季度': 320, '二季度': 180, '三季度': 300, '四季度': 405})\n",
    "ser2"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(ser2[0], ser2[2], ser2[-1])\n",
    "ser2[0], ser2[-1] = 350, 360 \n",
    "print(ser2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(ser2['一季度'], ser2['三季度'])\n",
    "ser2['一季度'] = 380\n",
    "print(ser2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(ser2[1:3])\n",
    "print(ser2['二季度': '四季度'])\n",
    "ser2[1:3] = 400, 500\n",
    "print(ser2)"
   ],
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   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(ser2[['二季度', '四季度']])\n",
    "ser2[['二季度', '四季度']] = 500, 520\n",
    "print(ser2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(ser2[ser2 >= 500])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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())"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "ser2.describe()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "ser3 = pd.Series(data=['apple', 'banana', 'apple', 'pitaya', 'apple', 'pitaya', 'durian'])\n",
    "ser3.value_counts()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "ser4 = pd.Series(data=[10, 20, np.NaN, 30, np.NaN])\n",
    "ser4.dropna()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "ser4.fillna(value=40)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "ser4.fillna(method='ffill')"
   ],
   "outputs": [],
   "metadata": {}
  }
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