{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Import data file\n",
"finances_path = \"PyBank_Budget_Data.csv\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#Read in CSV file\n",
"finances_df = pd.read_csv(finances_path, encoding=\"utf-8\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Date | \n",
" Profit/Losses | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Jan-10 | \n",
" 867884 | \n",
"
\n",
" \n",
" 1 | \n",
" Feb-10 | \n",
" 984655 | \n",
"
\n",
" \n",
" 2 | \n",
" Mar-10 | \n",
" 322013 | \n",
"
\n",
" \n",
" 3 | \n",
" Apr-10 | \n",
" -69417 | \n",
"
\n",
" \n",
" 4 | \n",
" May-10 | \n",
" 310503 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Date Profit/Losses\n",
"0 Jan-10 867884\n",
"1 Feb-10 984655\n",
"2 Mar-10 322013\n",
"3 Apr-10 -69417\n",
"4 May-10 310503"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Test above statement\n",
"finances_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"86"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calculatet the total number of months included in the dataset\n",
"Date_count = len(finances_df[\"Date\"].unique())\n",
"Date_count"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"$ 38382578\n"
]
}
],
"source": [
"#Calculate the net total amount of \"Profit/Losses\" over the entire period\n",
"Total = finances_df[\"Profit/Losses\"].sum()\n",
"print (\"$\", Total)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Date | \n",
" Profit/Losses | \n",
" Difference | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Jan-10 | \n",
" 867884 | \n",
" NaN | \n",
"
\n",
" \n",
" 1 | \n",
" Feb-10 | \n",
" 984655 | \n",
" 116771.0 | \n",
"
\n",
" \n",
" 2 | \n",
" Mar-10 | \n",
" 322013 | \n",
" -662642.0 | \n",
"
\n",
" \n",
" 3 | \n",
" Apr-10 | \n",
" -69417 | \n",
" -391430.0 | \n",
"
\n",
" \n",
" 4 | \n",
" May-10 | \n",
" 310503 | \n",
" 379920.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Date Profit/Losses Difference\n",
"0 Jan-10 867884 NaN\n",
"1 Feb-10 984655 116771.0\n",
"2 Mar-10 322013 -662642.0\n",
"3 Apr-10 -69417 -391430.0\n",
"4 May-10 310503 379920.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Difference in profit or loss by row\n",
"Difference = finances_df [\"Profit/Losses\"].diff(+1)\n",
"#Add column \"Difference\" to dataframe\n",
"finances_df[\"Difference\"] = Difference\n",
"\n",
"finances_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"$ -2315\n"
]
}
],
"source": [
"#Calculate average change\n",
"Average_Change = finances_df[\"Difference\"].mean()\n",
"Average_Change = round(int(Average_Change))\n",
"print (\"$\", Average_Change)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"$ 1926159 Feb-12\n"
]
}
],
"source": [
"#The greatest increase in profits (date and amount) over the entire period\n",
"Greatest_Increase = finances_df[\"Difference\"].max()\n",
"Greatest_Increase = round(int(Greatest_Increase))\n",
"print(\"$\", Greatest_Increase, finances_df.loc[finances_df[\"Difference\"]== 1926159, \"Date\"].values[0])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"$ -2196167 Sep-13\n"
]
}
],
"source": [
"#The greatest decrease in profits (date and amount) over the entire period\n",
"Greatest_Decrease = finances_df[\"Difference\"].min()\n",
"Greatest_Decrease = round(int(Greatest_Decrease))\n",
"print(\"$\", Greatest_Decrease, finances_df.loc[finances_df[\"Difference\"]== -2196167, \"Date\"].values[0])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Financial Analysis\n",
"________________________________________________\n",
"\n",
"Total Months: 86\n",
"Total: $38382578\n",
"Average Change: $-2315\n",
"Greatest Increase: $ 1926159 Month: Feb-12\n",
"Greatest Decrease: $ -2196167 Month : Sep-13\n"
]
}
],
"source": [
"# Print results\n",
"print(\"Financial Analysis\")\n",
"print(\"________________________________________________\")\n",
"print(\"\")\n",
"print(f\"Total Months: {(Date_count)}\")\n",
"print(f\"Total: ${(Total)}\")\n",
"print(f\"Average Change: ${(Average_Change)}\")\n",
"print(\"Greatest Increase: $\", Greatest_Increase, \"Month: \",finances_df.loc[finances_df[\"Difference\"]== 1926159, \"Date\"].values[0])\n",
"print(\"Greatest Decrease: $\", Greatest_Decrease, \"Month : \",finances_df.loc[finances_df[\"Difference\"]== -2196167, \"Date\"].values[0])"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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