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[lab-intro-pandas] Letícia Graziela Demarchi Ferreira #223

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275 changes: 236 additions & 39 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -18,10 +18,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": []
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
Expand All @@ -41,10 +44,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 5.7\n",
"1 75.2\n",
"2 74.4\n",
"3 84.0\n",
"4 66.5\n",
"5 66.3\n",
"6 55.8\n",
"7 75.7\n",
"8 29.1\n",
"9 43.7\n",
"dtype: float64\n"
]
}
],
"source": [
"lst = [5.7, 75.2, 74.4, 84.0, 66.5, 66.3, 55.8, 75.7, 29.1, 43.7]\n",
"panda_series = pd.Series(lst)\n",
"print (panda_series)"
]
},
{
"cell_type": "markdown",
Expand All @@ -57,10 +82,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"74.4\n"
]
}
],
"source": [
"value_3 = panda_series[2]\n",
"\n",
"print(value_3)"
]
},
{
"cell_type": "markdown",
Expand All @@ -71,7 +108,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -89,10 +126,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3 4\n",
"0 53.1 95.0 67.5 35.0 78.4\n",
"1 61.3 40.8 30.8 37.8 87.6\n",
"2 20.6 73.2 44.2 14.6 91.8\n",
"3 57.4 0.1 96.1 4.2 69.5\n",
"4 83.6 20.5 85.4 22.8 35.9\n",
"5 49.0 69.0 0.1 31.8 89.1\n",
"6 23.3 40.7 95.0 83.8 26.9\n",
"7 27.6 26.4 53.8 88.8 68.5\n",
"8 96.6 96.4 53.4 72.4 50.1\n",
"9 73.7 39.0 43.2 81.6 34.7\n"
]
}
],
"source": [
"data_frame = pd.DataFrame(b)\n",
"\n",
"print(data_frame)"
]
},
{
"cell_type": "markdown",
Expand All @@ -103,7 +162,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -112,10 +171,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Score_1 Score_2 Score_3 Score_4 Score_5\n",
"0 53.1 95.0 67.5 35.0 78.4\n",
"1 61.3 40.8 30.8 37.8 87.6\n",
"2 20.6 73.2 44.2 14.6 91.8\n",
"3 57.4 0.1 96.1 4.2 69.5\n",
"4 83.6 20.5 85.4 22.8 35.9\n",
"5 49.0 69.0 0.1 31.8 89.1\n",
"6 23.3 40.7 95.0 83.8 26.9\n",
"7 27.6 26.4 53.8 88.8 68.5\n",
"8 96.6 96.4 53.4 72.4 50.1\n",
"9 73.7 39.0 43.2 81.6 34.7\n"
]
}
],
"source": [
"data_frame = pd.DataFrame(b)\n",
"data_frame.rename(columns={0: 'Score_1', 1: 'Score_2', 2: 'Score_3', 3: 'Score_4', 4: 'Score_5'}, inplace=True)\n",
"print (data_frame)"
]
},
{
"cell_type": "markdown",
Expand All @@ -126,10 +207,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Score_1 Score_3 Score_5\n",
"0 53.1 67.5 78.4\n",
"1 61.3 30.8 87.6\n",
"2 20.6 44.2 91.8\n",
"3 57.4 96.1 69.5\n",
"4 83.6 85.4 35.9\n",
"5 49.0 0.1 89.1\n",
"6 23.3 95.0 26.9\n",
"7 27.6 53.8 68.5\n",
"8 96.6 53.4 50.1\n",
"9 73.7 43.2 34.7\n"
]
}
],
"source": [
"subset_df = data_frame[['Score_1', 'Score_3', 'Score_5']]\n",
"\n",
"print(subset_df)"
]
},
{
"cell_type": "markdown",
Expand All @@ -140,10 +243,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"56.95000000000001\n"
]
}
],
"source": [
"avg_score_3 = data_frame['Score_3'].mean()\n",
"print (avg_score_3)"
]
},
{
"cell_type": "markdown",
Expand All @@ -154,10 +268,24 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"88.8"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_score_4 = data_frame['Score_4'].max()\n",
"max_score_4"
]
},
{
"cell_type": "markdown",
Expand All @@ -168,10 +296,24 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"40.75"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"median_score_2 = data_frame['Score_2'].median()\n",
"median_score_2"
]
},
{
"cell_type": "markdown",
Expand All @@ -182,7 +324,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -203,10 +345,31 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Description Quantity UnitPrice Revenue\n",
"0 LUNCH BAG APPLE DESIGN 1 1.65 1.65\n",
"1 SET OF 60 VINTAGE LEAF CAKE CASES 24 0.55 13.20\n",
"2 RIBBON REEL STRIPES DESIGN 1 1.65 1.65\n",
"3 WORLD WAR 2 GLIDERS ASSTD DESIGNS 2880 0.18 518.40\n",
"4 PLAYING CARDS JUBILEE UNION JACK 2 1.25 2.50\n",
"5 POPCORN HOLDER 7 0.85 5.95\n",
"6 BOX OF VINTAGE ALPHABET BLOCKS 1 11.95 11.95\n",
"7 PARTY BUNTING 4 4.95 19.80\n",
"8 JAZZ HEARTS ADDRESS BOOK 10 0.19 1.90\n",
"9 SET OF 4 SANTA PLACE SETTINGS 48 1.25 60.00\n"
]
}
],
"source": [
"orders_data_frame = pd.DataFrame(orders)\n",
"print(orders_data_frame)"
]
},
{
"cell_type": "markdown",
Expand All @@ -217,10 +380,25 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Quantity Ordered: 2978\n",
"Total Revenue Generated: 637.0\n"
]
}
],
"source": [
"total_quantity_ordered = orders_data_frame['Quantity'].sum()\n",
"total_revenue_generated = orders_data_frame['Revenue'].sum()\n",
"\n",
"print(\"Total Quantity Ordered:\", total_quantity_ordered)\n",
"print(\"Total Revenue Generated:\", total_revenue_generated)"
]
},
{
"cell_type": "markdown",
Expand All @@ -231,10 +409,29 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most expensive item price: 11.95\n",
"Least expensive item price: 0.18\n",
"Price difference: 11.77\n"
]
}
],
"source": [
"most_expensive = orders_data_frame['UnitPrice'].max()\n",
"least_expensive = orders_data_frame['UnitPrice'].min()\n",
"\n",
"price_difference = most_expensive - least_expensive\n",
"\n",
"print(\"Most expensive item price:\", most_expensive)\n",
"print(\"Least expensive item price:\", least_expensive)\n",
"print(\"Price difference:\", price_difference)"
]
}
],
"metadata": {
Expand All @@ -253,7 +450,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.11.2"
}
},
"nbformat": 4,
Expand Down