lab-6.ipynb

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   "source": [
    "## Lab 6\n",
    "### *Hasan Ravda [216270307028]*"
   ]
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   "source": [
    "# Write a Pandas program to implement following operation\n"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
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   "source": [
    "### 1. To convert a NumPy array to a Pandas series"
   ]
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       "0    H\n",
       "1    a\n",
       "2    s\n",
       "3    a\n",
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   "source": [
    "arr = np.array(list(\"Hasan\"))\n",
    "ser = pd.Series(arr)\n",
    "ser"
   ]
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   "source": [
    "### 2. To convert the first column of a DataFrame as a Series"
   ]
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   price product\n",
       "0    100       a\n",
       "1    200       b\n",
       "2    300       c\n",
       "3    400       d"
      ]
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    "df=pd.DataFrame({\n",
    "    \"price\":[100,200,300,400],\n",
    "    \"product\":[\"a\",\"b\",\"c\",\"d\"]\n",
    "})\n",
    "df"
   ]
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     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    100\n",
       "1    200\n",
       "2    300\n",
       "3    400\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 4,
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   "source": [
    "price_col = df['price']\n",
    "print(type(price_col))\n",
    "price_col"
   ]
  },
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   "source": [
    "### 3. To create the mean and standard deviation of the data of a given Series"
   ]
  },
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     "text": [
      "Series s1 :\n",
      "0     1\n",
      "1     2\n",
      "2     3\n",
      "3     4\n",
      "4     5\n",
      "5     6\n",
      "6     7\n",
      "7     8\n",
      "8     9\n",
      "9    10\n",
      "dtype: int64\n",
      "\n",
      "Mean =  5.5\n",
      "Standard deviation =  3.0276503540974917\n"
     ]
    }
   ],
   "source": [
    "s1 = pd.Series(np.arange(1,11))\n",
    "print(\"Series s1 :\")\n",
    "print(s1)\n",
    "\n",
    "print(\"\\nMean = \",s1.mean())\n",
    "print(\"Standard deviation = \",s1.std())"
   ]
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   "source": [
    "### 4. To sort a given Series "
   ]
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   "outputs": [
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     "text": [
      "Series s2 :\n",
      " 0     4\n",
      "1     2\n",
      "2    64\n",
      "3     7\n",
      "4     3\n",
      "5     9\n",
      "dtype: int64\n",
      "\n",
      "Series s2 After sorting :\n"
     ]
    },
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       "1     2\n",
       "4     3\n",
       "0     4\n",
       "3     7\n",
       "5     9\n",
       "2    64\n",
       "dtype: int64"
      ]
     },
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   ],
   "source": [
    "s2=pd.Series([4,2,64,7,3,9])\n",
    "print(\"Series s2 :\\n\",s2)\n",
    "print(\"\\nSeries s2 After sorting :\")\n",
    "s2.sort_values()"
   ]
  }
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