diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 406e6ba..2395c4c 100755
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -18,11 +18,12 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
- "# import libraries here"
+ "import pandas as pd\n",
+ "import numpy as np"
]
},
{
@@ -203,11 +204,155 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Name | \n",
+ " Department | \n",
+ " Education | \n",
+ " Gender | \n",
+ " Title | \n",
+ " Years | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Jose | \n",
+ " IT | \n",
+ " Bachelor | \n",
+ " M | \n",
+ " analyst | \n",
+ " 1 | \n",
+ " 35 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " Maria | \n",
+ " IT | \n",
+ " Master | \n",
+ " F | \n",
+ " analyst | \n",
+ " 2 | \n",
+ " 30 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " David | \n",
+ " HR | \n",
+ " Master | \n",
+ " M | \n",
+ " analyst | \n",
+ " 2 | \n",
+ " 30 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " Sonia | \n",
+ " HR | \n",
+ " Bachelor | \n",
+ " F | \n",
+ " analyst | \n",
+ " 4 | \n",
+ " 35 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " Samuel | \n",
+ " Sales | \n",
+ " Master | \n",
+ " M | \n",
+ " associate | \n",
+ " 3 | \n",
+ " 55 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " Eva | \n",
+ " Sales | \n",
+ " Bachelor | \n",
+ " F | \n",
+ " associate | \n",
+ " 2 | \n",
+ " 55 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " Carlos | \n",
+ " IT | \n",
+ " Master | \n",
+ " M | \n",
+ " VP | \n",
+ " 8 | \n",
+ " 70 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " Pedro | \n",
+ " IT | \n",
+ " Phd | \n",
+ " M | \n",
+ " associate | \n",
+ " 7 | \n",
+ " 60 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " Ana | \n",
+ " HR | \n",
+ " Master | \n",
+ " F | \n",
+ " VP | \n",
+ " 8 | \n",
+ " 70 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Name Department Education Gender Title Years Salary\n",
+ "0 Jose IT Bachelor M analyst 1 35\n",
+ "1 Maria IT Master F analyst 2 30\n",
+ "2 David HR Master M analyst 2 30\n",
+ "3 Sonia HR Bachelor F analyst 4 35\n",
+ "4 Samuel Sales Master M associate 3 55\n",
+ "5 Eva Sales Bachelor F associate 2 55\n",
+ "6 Carlos IT Master M VP 8 70\n",
+ "7 Pedro IT Phd M associate 7 60\n",
+ "8 Ana HR Master F VP 8 70"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees=pd.read_csv(\"Employee.csv\")\n",
+ "employees"
]
},
{
@@ -219,21 +364,53 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 9 entries, 0 to 8\n",
+ "Data columns (total 7 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Name 9 non-null object\n",
+ " 1 Department 9 non-null object\n",
+ " 2 Education 9 non-null object\n",
+ " 3 Gender 9 non-null object\n",
+ " 4 Title 9 non-null object\n",
+ " 5 Years 9 non-null int64 \n",
+ " 6 Salary 9 non-null int64 \n",
+ "dtypes: int64(2), object(5)\n",
+ "memory usage: 636.0+ bytes\n"
+ ]
+ }
+ ],
+ "source": [
+ "employees.info()"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'\\nData types from first glance seem corresponding to the context of each series in the dataframe\\n'"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"\"\"\"\n",
- "your comments here\n",
+ "Data types from first glance seem corresponding to the context of each series in the dataframe\n",
"\"\"\""
]
},
@@ -246,11 +423,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "48.888888888888886"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "employees[\"Salary\"].mean()"
]
},
{
@@ -262,11 +450,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "70"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "employees[\"Salary\"].max()"
]
},
{
@@ -278,11 +477,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "employees[\"Salary\"].min()"
]
},
{
@@ -294,11 +504,77 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Name | \n",
+ " Department | \n",
+ " Education | \n",
+ " Gender | \n",
+ " Title | \n",
+ " Years | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " Maria | \n",
+ " IT | \n",
+ " Master | \n",
+ " F | \n",
+ " analyst | \n",
+ " 2 | \n",
+ " 30 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " David | \n",
+ " HR | \n",
+ " Master | \n",
+ " M | \n",
+ " analyst | \n",
+ " 2 | \n",
+ " 30 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Name Department Education Gender Title Years Salary\n",
+ "1 Maria IT Master F analyst 2 30\n",
+ "2 David HR Master M analyst 2 30"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees[employees[\"Salary\"] == employees[\"Salary\"].min()]"
]
},
{
@@ -310,11 +586,66 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Name | \n",
+ " Department | \n",
+ " Education | \n",
+ " Gender | \n",
+ " Title | \n",
+ " Years | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2 | \n",
+ " David | \n",
+ " HR | \n",
+ " Master | \n",
+ " M | \n",
+ " analyst | \n",
+ " 2 | \n",
+ " 30 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Name Department Education Gender Title Years Salary\n",
+ "2 David HR Master M analyst 2 30"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees[employees[\"Name\"] == \"David\"]"
]
},
{
@@ -326,11 +657,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2 30\n",
+ "Name: Salary, dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "employees[employees[\"Name\"] == \"David\"][\"Salary\"]"
]
},
{
@@ -342,11 +685,88 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Name | \n",
+ " Department | \n",
+ " Education | \n",
+ " Gender | \n",
+ " Title | \n",
+ " Years | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 4 | \n",
+ " Samuel | \n",
+ " Sales | \n",
+ " Master | \n",
+ " M | \n",
+ " associate | \n",
+ " 3 | \n",
+ " 55 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " Eva | \n",
+ " Sales | \n",
+ " Bachelor | \n",
+ " F | \n",
+ " associate | \n",
+ " 2 | \n",
+ " 55 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " Pedro | \n",
+ " IT | \n",
+ " Phd | \n",
+ " M | \n",
+ " associate | \n",
+ " 7 | \n",
+ " 60 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Name Department Education Gender Title Years Salary\n",
+ "4 Samuel Sales Master M associate 3 55\n",
+ "5 Eva Sales Bachelor F associate 2 55\n",
+ "7 Pedro IT Phd M associate 7 60"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees[employees[\"Title\"] == \"associate\"]"
]
},
{
@@ -359,12 +779,89 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Name | \n",
+ " Department | \n",
+ " Education | \n",
+ " Gender | \n",
+ " Title | \n",
+ " Years | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Jose | \n",
+ " IT | \n",
+ " Bachelor | \n",
+ " M | \n",
+ " analyst | \n",
+ " 1 | \n",
+ " 35 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " Maria | \n",
+ " IT | \n",
+ " Master | \n",
+ " F | \n",
+ " analyst | \n",
+ " 2 | \n",
+ " 30 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " David | \n",
+ " HR | \n",
+ " Master | \n",
+ " M | \n",
+ " analyst | \n",
+ " 2 | \n",
+ " 30 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Name Department Education Gender Title Years Salary\n",
+ "0 Jose IT Bachelor M analyst 1 35\n",
+ "1 Maria IT Master F analyst 2 30\n",
+ "2 David HR Master M analyst 2 30"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Method 1\n",
- "# your code here"
+ "employees[employees.index<3]"
]
},
{
@@ -386,11 +883,66 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Name | \n",
+ " Department | \n",
+ " Education | \n",
+ " Gender | \n",
+ " Title | \n",
+ " Years | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 7 | \n",
+ " Pedro | \n",
+ " IT | \n",
+ " Phd | \n",
+ " M | \n",
+ " associate | \n",
+ " 7 | \n",
+ " 60 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Name Department Education Gender Title Years Salary\n",
+ "7 Pedro IT Phd M associate 7 60"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees[(employees[\"Title\"] == \"associate\") & (employees[\"Salary\"] > 55)]"
]
},
{
@@ -402,11 +954,84 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " | Years | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 35.000000 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 38.333333 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 55.000000 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 35.000000 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 60.000000 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 70.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Salary\n",
+ "Years \n",
+ "1 35.000000\n",
+ "2 38.333333\n",
+ "3 55.000000\n",
+ "4 35.000000\n",
+ "7 60.000000\n",
+ "8 70.000000"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees.groupby(\"Years\").agg({\"Salary\":\"mean\"})"
]
},
{
@@ -418,11 +1043,69 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " | Title | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | VP | \n",
+ " 70.000000 | \n",
+ "
\n",
+ " \n",
+ " | analyst | \n",
+ " 32.500000 | \n",
+ "
\n",
+ " \n",
+ " | associate | \n",
+ " 56.666667 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Salary\n",
+ "Title \n",
+ "VP 70.000000\n",
+ "analyst 32.500000\n",
+ "associate 56.666667"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees.groupby(\"Title\").agg({\"Salary\":\"mean\"})"
]
},
{
@@ -434,11 +1117,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "35.0 55.0 60.0\n"
+ ]
+ }
+ ],
"source": [
- "# your code here"
+ "q1=np.quantile(employees[\"Salary\"],0.25)\n",
+ "q2=np.quantile(employees[\"Salary\"],0.50)\n",
+ "q3=np.quantile(employees[\"Salary\"],0.75)\n",
+ "print (q1,q2,q3)"
]
},
{
@@ -450,11 +1144,64 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " | Gender | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | F | \n",
+ " 47.5 | \n",
+ "
\n",
+ " \n",
+ " | M | \n",
+ " 50.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Salary\n",
+ "Gender \n",
+ "F 47.5\n",
+ "M 50.0"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees.groupby(\"Gender\").agg({\"Salary\":\"mean\"})"
]
},
{
@@ -467,11 +1214,98 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Years | \n",
+ " Salary | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 9.000000 | \n",
+ " 9.000000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 4.111111 | \n",
+ " 48.888889 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 2.803767 | \n",
+ " 16.541194 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 1.000000 | \n",
+ " 30.000000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 2.000000 | \n",
+ " 35.000000 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 3.000000 | \n",
+ " 55.000000 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 7.000000 | \n",
+ " 60.000000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 8.000000 | \n",
+ " 70.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Years Salary\n",
+ "count 9.000000 9.000000\n",
+ "mean 4.111111 48.888889\n",
+ "std 2.803767 16.541194\n",
+ "min 1.000000 30.000000\n",
+ "25% 2.000000 35.000000\n",
+ "50% 3.000000 55.000000\n",
+ "75% 7.000000 60.000000\n",
+ "max 8.000000 70.000000"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "employees.describe()"
]
},
{
@@ -484,11 +1318,82 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " min | \n",
+ " max | \n",
+ " diff | \n",
+ "
\n",
+ " \n",
+ " | Department | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | HR | \n",
+ " 30 | \n",
+ " 70 | \n",
+ " 40 | \n",
+ "
\n",
+ " \n",
+ " | IT | \n",
+ " 30 | \n",
+ " 70 | \n",
+ " 40 | \n",
+ "
\n",
+ " \n",
+ " | Sales | \n",
+ " 55 | \n",
+ " 55 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " min max diff\n",
+ "Department \n",
+ "HR 30 70 40\n",
+ "IT 30 70 40\n",
+ "Sales 55 55 0"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_emp=employees.groupby(\"Department\").agg({\"Salary\":[\"min\",\"max\"]})\n",
+ "new_emp.columns=[\"min\",\"max\"]\n",
+ "new_emp[\"diff\"]=new_emp[\"max\"]-new_emp[\"min\"]\n",
+ "new_emp"
]
},
{
@@ -799,7 +1704,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.11.4"
}
},
"nbformat": 4,