diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index f50ae3d..7e89d0e 100755
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -18,11 +18,12 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
- "# your code here"
+ "import numpy as np\n",
+ "import pandas as pd"
]
},
{
@@ -34,7 +35,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -43,11 +44,11 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
- "# your code here"
+ "s_lst = pd.Series(lst)"
]
},
{
@@ -61,11 +62,22 @@
},
{
"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"
+ "s_lst[2]"
]
},
{
@@ -77,7 +89,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -95,11 +107,144 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
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+ "
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+ " \n",
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+ "
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+ " \n",
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+ " \n",
+ " 3 | \n",
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+ " 4.2 | \n",
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+ "
\n",
+ " \n",
+ " 4 | \n",
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+ " 85.4 | \n",
+ " 22.8 | \n",
+ " 35.9 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 49.0 | \n",
+ " 69.0 | \n",
+ " 0.1 | \n",
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+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 23.3 | \n",
+ " 40.7 | \n",
+ " 95.0 | \n",
+ " 83.8 | \n",
+ " 26.9 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 27.6 | \n",
+ " 26.4 | \n",
+ " 53.8 | \n",
+ " 88.8 | \n",
+ " 68.5 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " 96.6 | \n",
+ " 96.4 | \n",
+ " 53.4 | \n",
+ " 72.4 | \n",
+ " 50.1 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " 73.7 | \n",
+ " 39.0 | \n",
+ " 43.2 | \n",
+ " 81.6 | \n",
+ " 34.7 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
+ " 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"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "dfb = pd.DataFrame(b)\n",
+ "dfb"
]
},
{
@@ -111,7 +256,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -120,11 +265,152 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
- "# your code here"
+ "dfb.columns = colnames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Score_1 | \n",
+ " Score_2 | \n",
+ " Score_3 | \n",
+ " Score_4 | \n",
+ " Score_5 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 53.1 | \n",
+ " 95.0 | \n",
+ " 67.5 | \n",
+ " 35.0 | \n",
+ " 78.4 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 61.3 | \n",
+ " 40.8 | \n",
+ " 30.8 | \n",
+ " 37.8 | \n",
+ " 87.6 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 20.6 | \n",
+ " 73.2 | \n",
+ " 44.2 | \n",
+ " 14.6 | \n",
+ " 91.8 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 57.4 | \n",
+ " 0.1 | \n",
+ " 96.1 | \n",
+ " 4.2 | \n",
+ " 69.5 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 83.6 | \n",
+ " 20.5 | \n",
+ " 85.4 | \n",
+ " 22.8 | \n",
+ " 35.9 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 49.0 | \n",
+ " 69.0 | \n",
+ " 0.1 | \n",
+ " 31.8 | \n",
+ " 89.1 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 23.3 | \n",
+ " 40.7 | \n",
+ " 95.0 | \n",
+ " 83.8 | \n",
+ " 26.9 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 27.6 | \n",
+ " 26.4 | \n",
+ " 53.8 | \n",
+ " 88.8 | \n",
+ " 68.5 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " 96.6 | \n",
+ " 96.4 | \n",
+ " 53.4 | \n",
+ " 72.4 | \n",
+ " 50.1 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " 73.7 | \n",
+ " 39.0 | \n",
+ " 43.2 | \n",
+ " 81.6 | \n",
+ " 34.7 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 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"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dfb"
]
},
{
@@ -136,11 +422,122 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Score_1 | \n",
+ " Score_3 | \n",
+ " Score_5 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 53.1 | \n",
+ " 67.5 | \n",
+ " 78.4 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 61.3 | \n",
+ " 30.8 | \n",
+ " 87.6 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 20.6 | \n",
+ " 44.2 | \n",
+ " 91.8 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 57.4 | \n",
+ " 96.1 | \n",
+ " 69.5 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 83.6 | \n",
+ " 85.4 | \n",
+ " 35.9 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 49.0 | \n",
+ " 0.1 | \n",
+ " 89.1 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 23.3 | \n",
+ " 95.0 | \n",
+ " 26.9 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 27.6 | \n",
+ " 53.8 | \n",
+ " 68.5 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " 96.6 | \n",
+ " 53.4 | \n",
+ " 50.1 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " 73.7 | \n",
+ " 43.2 | \n",
+ " 34.7 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 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"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "sub_dfb = dfb[['Score_1','Score_3','Score_5']]\n",
+ "sub_dfb"
]
},
{
@@ -152,11 +549,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "56.95000000000001"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "dfb['Score_3'].mean()"
]
},
{
@@ -168,11 +576,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "88.8"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "dfb['Score_4'].max()"
]
},
{
@@ -184,11 +603,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "40.75"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "dfb['Score_2'].median()"
]
},
{
@@ -200,7 +630,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -221,11 +651,133 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Description | \n",
+ " Quantity | \n",
+ " UnitPrice | \n",
+ " Revenue | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " LUNCH BAG APPLE DESIGN | \n",
+ " 1 | \n",
+ " 1.65 | \n",
+ " 1.65 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " SET OF 60 VINTAGE LEAF CAKE CASES | \n",
+ " 24 | \n",
+ " 0.55 | \n",
+ " 13.20 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " RIBBON REEL STRIPES DESIGN | \n",
+ " 1 | \n",
+ " 1.65 | \n",
+ " 1.65 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " WORLD WAR 2 GLIDERS ASSTD DESIGNS | \n",
+ " 2880 | \n",
+ " 0.18 | \n",
+ " 518.40 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " PLAYING CARDS JUBILEE UNION JACK | \n",
+ " 2 | \n",
+ " 1.25 | \n",
+ " 2.50 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " POPCORN HOLDER | \n",
+ " 7 | \n",
+ " 0.85 | \n",
+ " 5.95 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " BOX OF VINTAGE ALPHABET BLOCKS | \n",
+ " 1 | \n",
+ " 11.95 | \n",
+ " 11.95 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " PARTY BUNTING | \n",
+ " 4 | \n",
+ " 4.95 | \n",
+ " 19.80 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " JAZZ HEARTS ADDRESS BOOK | \n",
+ " 10 | \n",
+ " 0.19 | \n",
+ " 1.90 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " SET OF 4 SANTA PLACE SETTINGS | \n",
+ " 48 | \n",
+ " 1.25 | \n",
+ " 60.00 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 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"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "products_df = pd.DataFrame(orders)\n",
+ "products_df"
]
},
{
@@ -237,11 +789,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Quantity 2978.0\n",
+ "Revenue 637.0\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "products_df[['Quantity','Revenue']].sum()"
]
},
{
@@ -253,11 +818,27 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "11.77"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "max_price = products_df['UnitPrice'].max()\n",
+ "min_price = products_df['UnitPrice'].min()\n",
+ "difference = max_price-min_price\n",
+ "max_price\n",
+ "min_price\n",
+ "difference"
]
}
],
@@ -277,7 +858,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.8.3"
}
},
"nbformat": 4,