01_pandas_tips and tricks.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1- How to see pandas and python versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2- Make a dataframe using dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "   a  b\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"a\":[1 , 2 ,3], \"b\" :[4,5,6]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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      "text/plain": [
       "   a  b\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
      ]
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
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   "execution_count": 7,
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   "outputs": [
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       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "   a  b\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
      ]
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "   0  1  2\n",
       "0  1  2  3\n",
       "1  4  5  6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "#using numpy array to create dataframe\n",
    "import numpy as np\n",
    "arr = np.array([[1,2,3],[4,5,6]])\n",
    "pd.DataFrame(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.072598</td>\n",
       "      <td>0.439058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.109214</td>\n",
       "      <td>0.377638</td>\n",
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      "text/plain": [
       "          0         1\n",
       "0  0.072598  0.439058\n",
       "1  0.109214  0.377638"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#numpy array dataframe\n",
    "pd.DataFrame(np.random.rand(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#columns name assign and row name\n",
    "df1 = pd.DataFrame(np.random.rand(4,6),columns = list(\"ABCDEF\"),index = [\"aa\",\"bb\",\"cc\",\"dd\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3- How to rename column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>aa</th>\n",
       "      <td>0.026295</td>\n",
       "      <td>0.934688</td>\n",
       "      <td>0.550317</td>\n",
       "      <td>0.520203</td>\n",
       "      <td>0.219354</td>\n",
       "      <td>0.436767</td>\n",
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       "    <tr>\n",
       "      <th>bb</th>\n",
       "      <td>0.153424</td>\n",
       "      <td>0.891169</td>\n",
       "      <td>0.305344</td>\n",
       "      <td>0.469939</td>\n",
       "      <td>0.554908</td>\n",
       "      <td>0.108508</td>\n",
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       "    <tr>\n",
       "      <th>cc</th>\n",
       "      <td>0.743319</td>\n",
       "      <td>0.269590</td>\n",
       "      <td>0.405183</td>\n",
       "      <td>0.468723</td>\n",
       "      <td>0.166960</td>\n",
       "      <td>0.747973</td>\n",
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       "    <tr>\n",
       "      <th>dd</th>\n",
       "      <td>0.055994</td>\n",
       "      <td>0.751790</td>\n",
       "      <td>0.334481</td>\n",
       "      <td>0.319423</td>\n",
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       "           A         B         C         D         E         F\n",
       "aa  0.026295  0.934688  0.550317  0.520203  0.219354  0.436767\n",
       "bb  0.153424  0.891169  0.305344  0.469939  0.554908  0.108508\n",
       "cc  0.743319  0.269590  0.405183  0.468723  0.166960  0.747973\n",
       "dd  0.055994  0.751790  0.334481  0.319423  0.315693  0.374547"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.rename(columns = {'A' : 'a','B' : 'b', 'C' : 'c', 'D' : 'd', 'F' : 'f'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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       "      <th>bb</th>\n",
       "      <td>0.153424</td>\n",
       "      <td>0.891169</td>\n",
       "      <td>0.305344</td>\n",
       "      <td>0.469939</td>\n",
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       "      <th>cc</th>\n",
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       "      <th>dd</th>\n",
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       "           a         b         c         d         E         f\n",
       "aa  0.026295  0.934688  0.550317  0.520203  0.219354  0.436767\n",
       "bb  0.153424  0.891169  0.305344  0.469939  0.554908  0.108508\n",
       "cc  0.743319  0.269590  0.405183  0.468723  0.166960  0.747973\n",
       "dd  0.055994  0.751790  0.334481  0.319423  0.315693  0.374547"
      ]
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     "execution_count": 28,
     "metadata": {},
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   "source": [
    "df1"
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   "cell_type": "code",
   "execution_count": 31,
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       "      <th>babaa</th>\n",
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      "text/plain": [
       "   babaa  babab\n",
       "0      1      4\n",
       "1      2      5\n",
       "2      3      6"
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   "source": [
    "df.add_prefix(\"baba\")"
   ]
  },
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   "execution_count": null,
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