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MohdAzamSayeed/DPA - Day 1.ipynb

Created November 12, 2020 14:11
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DPA - Day 1 Notebook
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DPA - Day 1.ipynb
{
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{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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"output_type": "stream",
"text": [
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n"
]
}
],
"source": [
"import numpy as np\n",
"import seaborn as sns\n",
"import os\n",
"import pandas as pd\n",
"\n",
"df = pd.DataFrame()\n",
"print (df)"
]
},
{
"cell_type": "code",
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"metadata": {},
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"name": "stdout",
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" 0\n",
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n"
]
}
],
"source": [
"import pandas as pd\n",
"data = [1,2,3,4,5]\n",
"df = pd.DataFrame(data)\n",
"print (df)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Item Quantity\n",
"0 Mouse 10\n",
"1 KBD 12\n",
"2 Monitor 13\n"
]
}
],
"source": [
"import pandas as pd\n",
"data = [['Mouse',10],['KBD',12],['Monitor',13]]\n",
"df = pd.DataFrame(data,columns=['Item','Quantity'])\n",
"print (df)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Name Quantity\n",
"0 Mouse 10.0\n",
"1 KBD 12.0\n",
"2 Monitor 13.0\n"
]
}
],
"source": [
"import pandas as pd\n",
"data = [['Mouse',10],['KBD',12],['Monitor',13]]\n",
"df = pd.DataFrame(data,columns=['Name','Quantity'],dtype=float)\n",
"print (df)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Name Age\n",
"0 Tom 28\n",
"1 Jack 34\n",
"2 Steve 29\n",
"3 Ricky 42\n"
]
}
],
"source": [
"import pandas as pd\n",
"data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}\n",
"df = pd.DataFrame(data)\n",
"print (df)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Name Age\n",
"rank1 Tom 28\n",
"rank2 Jack 34\n",
"rank3 Steve 29\n",
"rank4 Ricky 42\n"
]
}
],
"source": [
"import pandas as pd\n",
"data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}\n",
"df = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])\n",
"print (df)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['.ipynb_checkpoints', 'box office for teaching.ipynb', 'cancer diagnosis for teaching.ipynb', 'data2.csv', 'Day 8', 'DPA - Day 1.ipynb', 'DPA-Class 2-13-09-2020.ipynb', 'gapminder.ipynb', 'temp.csv', 'test.csv', 'train.csv']\n"
]
}
],
"source": [
"print(os.listdir())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" </tr>\n",
" <tr>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>na</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" A B\n",
"0 1 1.0\n",
"1 NaN NaN\n",
"2 3 31.0\n",
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"5 1 11.0\n",
"6 2 21.0\n",
"7 NaN 24.0\n",
"8 1 12.0\n",
"9 na 32.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.read_csv(\"temp.csv\")\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <td>False</td>\n",
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" <tr>\n",
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" <td>False</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>True</td>\n",
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" <th>8</th>\n",
" <td>False</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>False</td>\n",
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" </tr>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" A B\n",
"0 False False\n",
"1 True True\n",
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"3 False False\n",
"4 False False\n",
"5 False False\n",
"6 False False\n",
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"8 False False\n",
"9 False False"
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},
"execution_count": 10,
"metadata": {},
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],
"source": [
"df.isnull()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A 2\n",
"B 1\n",
"dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"missing_values=[\"N/a\",\"na\",np.nan]\n",
"df=pd.read_csv(\"temp.csv\",na_values=missing_values)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <td>22.0</td>\n",
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" <tr>\n",
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" <td>3.0</td>\n",
" <td>33.0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>1.0</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2.0</td>\n",
" <td>21.0</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>1.0</td>\n",
" <td>12.0</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>NaN</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" A B\n",
"0 1.0 1.0\n",
"1 NaN NaN\n",
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"6 2.0 21.0\n",
"7 NaN 24.0\n",
"8 1.0 12.0\n",
"9 NaN 32.0"
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},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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"</div>"
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"text/plain": [
" A B\n",
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"5 False False\n",
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"9 True False"
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},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A 3\n",
"B 1\n",
"dtype: int64"
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},
"execution_count": 15,
"metadata": {},
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],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
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"B True\n",
"dtype: bool"
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},
"execution_count": 16,
"metadata": {},
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"source": [
"df.isnull().any()"
]
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{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2086359aa00>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(df.isnull(),yticklabels=False)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x20863d5cc40>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(df.isnull(),yticklabels=False,annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <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>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 1.0 2.0\n",
"1 NaN NaN\n",
"2 1.0 4.0\n",
"3 2.0 NaN\n",
"4 3.0 22.0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df11 = pd.DataFrame(data={\"A\":[1,np.nan,1,2,3],\n",
" \"B\":[2,np.nan,4,np.nan,22]\n",
" })\n",
"df11"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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" <th>0</th>\n",
" <td>1.0</td>\n",
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" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>22.0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 1.0 2.0\n",
"2 1.0 4.0\n",
"4 3.0 22.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df11.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>22.0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 1.0 2.0\n",
"2 1.0 4.0\n",
"3 2.0 NaN\n",
"4 3.0 22.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df11.dropna(how=\"all\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe tbody tr th:only-of-type {\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 1.0 2.0\n",
"1 0.0 0.0\n",
"2 1.0 4.0\n",
"3 2.0 0.0\n",
"4 3.0 22.0"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df11.fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe tbody tr th:only-of-type {\n",
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" <td>2.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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