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[lab-list-comprehensions]Valentin Kalinov #171

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ta-data-pt-rmt Feb 24, 2022
242c2b6
Valentin Kalinov
kalinof Feb 26, 2022
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ValentinKalinov
kalinof Mar 1, 2022
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144 changes: 112 additions & 32 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -29,10 +29,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]\n"
]
}
],
"source": [
"newlist = [x for x in range(1,51)]\n",
"print(newlist)"
]
},
{
"cell_type": "markdown",
Expand All @@ -43,25 +54,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200]\n"
]
}
],
"source": [
"even_numbers = [x for x in range(2,202) if x % 2 == 0]\n",
"print(even_numbers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Use a list comprehension to create and print a list containing all elements of the 10 x 4 Numpy array below."
"## 3. Use a list comprehension to create and print a list containing all elements of the 10 x 4 list below.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"a = np.array([[0.84062117, 0.48006452, 0.7876326 , 0.77109654],\n",
"a = [[0.84062117, 0.48006452, 0.7876326 , 0.77109654],\n",
" [0.44409793, 0.09014516, 0.81835917, 0.87645456],\n",
" [0.7066597 , 0.09610873, 0.41247947, 0.57433389],\n",
" [0.29960807, 0.42315023, 0.34452557, 0.4751035 ],\n",
Expand All @@ -70,7 +92,25 @@
" [0.71725408, 0.87702738, 0.31244595, 0.76615487],\n",
" [0.20754036, 0.57871812, 0.07214068, 0.40356048],\n",
" [0.12149553, 0.53222417, 0.9976855 , 0.12536346],\n",
" [0.80930099, 0.50962849, 0.94555126, 0.33364763]])"
" [0.80930099, 0.50962849, 0.94555126, 0.33364763]]"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.84062117, 0.48006452, 0.7876326, 0.77109654, 0.44409793, 0.09014516, 0.81835917, 0.87645456, 0.7066597, 0.09610873, 0.41247947, 0.57433389, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.92796258, 0.69814654, 0.41290051, 0.19561071, 0.16284783, 0.97016248, 0.71725408, 0.87702738, 0.31244595, 0.76615487, 0.20754036, 0.57871812, 0.07214068, 0.40356048, 0.12149553, 0.53222417, 0.9976855, 0.12536346, 0.80930099, 0.50962849, 0.94555126, 0.33364763]\n"
]
}
],
"source": [
"bb = [bc for i in a for bc in i]\n",
"print(bb)"
]
},
{
Expand All @@ -80,6 +120,13 @@
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -89,25 +136,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.84062117, 0.7876326, 0.77109654, 0.81835917, 0.87645456, 0.7066597, 0.57433389, 0.92796258, 0.69814654, 0.97016248, 0.71725408, 0.87702738, 0.76615487, 0.57871812, 0.53222417, 0.9976855, 0.80930099, 0.50962849, 0.94555126]\n"
]
}
],
"source": [
"cc = [bc for i in a for bc in i if bc > 0.5]\n",
"print(cc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Use a list comprehension to create and print a list containing all elements of the 5 x 2 x 3 Numpy array below."
"### 5. Use a list comprehension to create and print a list containing all elements of the 5 x 2 x 3 list below."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"b = np.array([[[0.55867166, 0.06210792, 0.08147297],\n",
"b = [[[0.55867166, 0.06210792, 0.08147297],\n",
" [0.82579068, 0.91512478, 0.06833034]],\n",
"\n",
" [[0.05440634, 0.65857693, 0.30296619],\n",
Expand All @@ -120,35 +178,57 @@
" [0.8694668 , 0.65669313, 0.10708681]],\n",
"\n",
" [[0.07529684, 0.46470767, 0.47984544],\n",
" [0.65368638, 0.14901286, 0.23760688]]])"
" [0.65368638, 0.14901286, 0.23760688]]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.55867166, 0.06210792, 0.08147297, 0.82579068, 0.91512478, 0.06833034, 0.05440634, 0.65857693, 0.30296619, 0.06769833, 0.96031863, 0.51293743, 0.09143215, 0.71893382, 0.45850679, 0.58256464, 0.59005654, 0.56266457, 0.71600294, 0.87392666, 0.11434044, 0.8694668, 0.65669313, 0.10708681, 0.07529684, 0.46470767, 0.47984544, 0.65368638, 0.14901286, 0.23760688]\n"
]
}
],
"source": [
"ccc = [cd for i in b for bc in i for cd in bc]\n",
"print(ccc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6. Add a condition to the list comprehension above so that the last value in each subarray is printed, but only if it is less than or equal to 0.5."
"### 6. Add a condition to the list comprehension above so that the last value in each sublist is printed, but only if it is less than or equal to 0.5."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.55867166, 0.82579068, 0.91512478, 0.65857693, 0.96031863, 0.51293743, 0.71893382, 0.58256464, 0.59005654, 0.56266457, 0.71600294, 0.87392666, 0.8694668, 0.65669313, 0.65368638]\n"
]
}
],
"source": [
"ccc = [cd for i in b for bc in i for cd in bc if cd > 0.5]\n",
"print(ccc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7. Use a list comprehension to select and print the names of all CSV files in the */data* directory."
"### 7. (optional - revisit once we cover this material) Use a list comprehension to select and print the names of all CSV files in the */data* directory."
]
},
{
Expand All @@ -162,7 +242,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### 8. Use a list comprehension and the Pandas `read_csv` and `concat` methods to read all CSV files in the */data* directory and combine them into a single data frame. Display the top 10 rows of the resulting data frame."
"### 8. (optional - revisit once we cover this material) Use a list comprehension and the Pandas `read_csv` and `concat` methods to read all CSV files in the */data* directory and combine them into a single data frame. Display the top 10 rows of the resulting data frame."
]
},
{
Expand All @@ -176,7 +256,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### 9. Use a list comprehension to select and print the column numbers for columns from the data set whose median is less than 0.48."
"### 9. (optional - revisit once we cover this material) Use a list comprehension to select and print the column numbers for columns from the data set whose median is less than 0.48."
]
},
{
Expand All @@ -190,7 +270,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### 10. Use a list comprehension to add a new column (20) to the data frame whose values are the values in column 19 minus 0.1. Display the top 10 rows of the resulting data frame."
"### 10. (optional - revisit once we cover this material) Use a list comprehension to add a new column (20) to the data frame whose values are the values in column 19 minus 0.1. Display the top 10 rows of the resulting data frame."
]
},
{
Expand All @@ -204,7 +284,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### 11. Use a list comprehension to extract and print all values from the data set that are between 0.7 and 0.75."
"### 11. (optional - revisit once we cover this material) Use a list comprehension to extract and print all values from the data set that are between 0.7 and 0.75."
]
},
{
Expand All @@ -217,7 +297,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -231,7 +311,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.9.7"
}
},
"nbformat": 4,
Expand Down