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submitted #79

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FilipeMiguelSantos Oct 27, 2020
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FilipeMiguelSantos Nov 1, 2020
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254 changes: 214 additions & 40 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -18,11 +18,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"# your code here\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
Expand All @@ -34,7 +36,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -43,11 +45,31 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"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": [
"# your code here"
"# your code here\n",
"serie1 = pd.Series(lst)\n",
"print(serie1)"
]
},
{
Expand All @@ -61,11 +83,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"74.4"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"# your code here\n",
"serie1[2]"
]
},
{
Expand All @@ -77,7 +111,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -95,11 +129,31 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"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": [
"# your code here"
"# your code here\n",
"new_df = pd.DataFrame(b)\n",
"print(new_df)"
]
},
{
Expand All @@ -111,7 +165,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -120,11 +174,31 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"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": [
"# your code here"
"# your code here\n",
"new_df.columns=colnames\n",
"print(new_df)"
]
},
{
Expand All @@ -136,11 +210,31 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"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": [
"# your code here"
"# your code here\n",
"new_df_sub = new_df[['Score_1','Score_3','Score_5']]\n",
"print(new_df)"
]
},
{
Expand All @@ -152,11 +246,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"56.95000000000001"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"# your code here\n",
"new_df['Score_3'].mean()"
]
},
{
Expand All @@ -168,11 +274,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"88.8"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"# your code here\n",
"new_df['Score_4'].max()"
]
},
{
Expand All @@ -184,11 +302,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"40.75"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"# your code here\n",
"new_df['Score_2'].median()"
]
},
{
Expand All @@ -200,7 +330,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -221,11 +351,31 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"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": [
"# your code here"
"# your code here\n",
"dt_frame_orders = pd.DataFrame(orders)\n",
"print(dt_frame_orders)"
]
},
{
Expand All @@ -237,11 +387,24 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total quantity: 2978\n",
"total revenue: 637.0\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"total_quantity = dt_frame_orders['Quantity'].sum()\n",
"print('total quantity: ', total_quantity)\n",
"total_revenue = dt_frame_orders['Revenue'].sum()\n",
"print('total revenue: ', total_revenue)\n"
]
},
{
Expand All @@ -253,11 +416,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"11.77\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"maximum = dt_frame_orders['UnitPrice'].max()\n",
"minimum = dt_frame_orders['UnitPrice'].min()\n",
"print(maximum-minimum)"
]
}
],
Expand All @@ -277,7 +451,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.7.6"
}
},
"nbformat": 4,
Expand Down