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LIS [Lab-Sql] CMendes #45

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8594a9f
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carlarsmendes Jul 20, 2019
0d226ed
changes
carlarsmendes Jul 20, 2019
88c484a
sql create and seed
carlarsmendes Jul 20, 2019
24226c6
aded delete and update
carlarsmendes Jul 20, 2019
f89ae0b
updated numpy lap
carlarsmendes Jul 23, 2019
4639abc
finished panda lab
carlarsmendes Jul 24, 2019
dc6dd49
Merge pull request #2 from carlarsmendes/lab-intro-pandas
carlarsmendes Jul 24, 2019
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241 changes: 202 additions & 39 deletions module-1/lab-intro-pandas/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 @@ -32,7 +35,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -41,10 +44,31 @@
},
{
"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": [
"pd_series = pd.Series(lst)\n",
"print(pd_series)"
]
},
{
"cell_type": "markdown",
Expand All @@ -57,10 +81,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"74.4\n"
]
}
],
"source": [
"pd_series[2]"
]
},
{
"cell_type": "markdown",
Expand All @@ -71,7 +105,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -89,10 +123,30 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"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": [
"pd_df = pd.DataFrame(b)"
]
},
{
"cell_type": "markdown",
Expand All @@ -103,7 +157,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -112,10 +166,30 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"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": [
"pd_df.columns = colnames"
]
},
{
"cell_type": "markdown",
Expand All @@ -126,10 +200,30 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"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": [
"pd_df_subset135 = pd_df[['Score_1','Score_3','Score_5']]"
]
},
{
"cell_type": "markdown",
Expand All @@ -140,10 +234,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score_3 56.95\n",
"dtype: float64\n"
]
}
],
"source": [
"pd_df_Sc3_mean = pd_df[['Score_3']].mean()"
]
},
{
"cell_type": "markdown",
Expand All @@ -154,10 +259,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score_4 88.8\n",
"dtype: float64\n"
]
}
],
"source": [
"pd_df_Sc4_max = pd_df[['Score_4']].max()"
]
},
{
"cell_type": "markdown",
Expand All @@ -168,10 +284,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score_2 40.75\n",
"dtype: float64\n"
]
}
],
"source": [
"pd_df_Sc2_median = pd_df[['Score_2']].median()"
]
},
{
"cell_type": "markdown",
Expand All @@ -182,7 +309,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -203,10 +330,30 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Description Quantity Revenue UnitPrice\n",
"0 LUNCH BAG APPLE DESIGN 1 1.65 1.65\n",
"1 SET OF 60 VINTAGE LEAF CAKE CASES 24 13.20 0.55\n",
"2 RIBBON REEL STRIPES DESIGN 1 1.65 1.65\n",
"3 WORLD WAR 2 GLIDERS ASSTD DESIGNS 2880 518.40 0.18\n",
"4 PLAYING CARDS JUBILEE UNION JACK 2 2.50 1.25\n",
"5 POPCORN HOLDER 7 5.95 0.85\n",
"6 BOX OF VINTAGE ALPHABET BLOCKS 1 11.95 11.95\n",
"7 PARTY BUNTING 4 19.80 4.95\n",
"8 JAZZ HEARTS ADDRESS BOOK 10 1.90 0.19\n",
"9 SET OF 4 SANTA PLACE SETTINGS 48 60.00 1.25\n"
]
}
],
"source": [
"df_orders = pd.DataFrame(orders)"
]
},
{
"cell_type": "markdown",
Expand All @@ -217,10 +364,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": []
"source": [
"total_quantity = df_orders['Quantity'].sum()\n",
"total_revenue = df_orders['Revenue'].sum()"
]
},
{
"cell_type": "markdown",
Expand All @@ -231,10 +381,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"11.77\n"
]
}
],
"source": [
"most_expensive = df_orders['UnitPrice'].max()\n",
"least_expensive = df_orders['UnitPrice'].min()\n",
"difference = most_expensive - least_expensive\n",
"print(difference)"
]
}
],
"metadata": {
Expand All @@ -253,7 +416,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
"version": "3.6.8"
}
},
"nbformat": 4,
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