test.ipynb

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     "text": [
      "Pearson Correlation Coefficient:  0.5940885257860046 and a P-value of: 0.4059114742139954\n"
     ]
    }
   ],
   "source": [
    "data = {'list 1':[1,2,3,2],'list 2':[2,5,5,1]}\n",
    "\n",
    "import pandas as pd #To Convert your lists to pandas data frames convert your lists into pandas dataframes\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['list 1','list 2'])\n",
    "\n",
    "from scipy import stats # For in-built method to get PCC\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df[\"list 1\"], df[\"list 2\"]) #define the columns to perform calculations on\n",
    "print(\"Pearson Correlation Coefficient: \", pearson_coef, \"and a P-value of:\", p_value) # Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.25"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2+5+5+1)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hahaha ha']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'hahaha ha'.split('00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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