ASPP 2024 material

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Pietro Berkes 2024-08-27 15:27:53 +03:00
commit 1f6bc07c51
90 changed files with 91689 additions and 0 deletions

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exercises/tabular_join/.DS_Store vendored Normal file

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{
"cells": [
{
"cell_type": "markdown",
"id": "f11a76bf",
"metadata": {},
"source": [
"# Exercise: Add experiment information to electrophysiology data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b6f2742b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# Set some Pandas options: maximum number of rows/columns it's going to display\n",
"pd.set_option('display.max_rows', 1000)\n",
"pd.set_option('display.max_columns', 100)"
]
},
{
"cell_type": "markdown",
"id": "2967c84e",
"metadata": {},
"source": [
"# Load electrophysiology data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ed626ee3",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../../data/QC_passed_2024-07-04_collected.csv')\n",
"info = pd.read_csv('../../data/op_info.csv')"
]
},
{
"cell_type": "markdown",
"id": "2fef4d37",
"metadata": {},
"source": [
"# 1. Add experiment information to the electrophysiology results\n",
"\n",
"* Is there information for every experiment?\n",
"* How many experiments did each patcher perform? (i.e., individual OPs, or rows in `info`)\n",
"* How many samples did each patcher analyze? (i.e., individual rows in `df`)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f3f57eb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "44031178",
"metadata": {},
"source": [
"# 2. Remove outliers from the table\n",
"\n",
"1. Load the list of outliers in `outliers.csv`\n",
"2. Use an anti-join to remove the outliers from the table\n",
"3. How many samples (rows) are left in the data?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7fa953af",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "84270332",
"metadata": {},
"source": [
"# 3. Save final result in `processed_QC_passed_2024-07-04_collected_v1.csv`\n",
"\n",
"1. Using the `.to_csv` method of Pandas DataFrames"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7bcff45",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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OP230808,23808S2c6
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OP220426,22427S4c7
OP230523,23523S2c1
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OP221027,22o27S1c2
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OP220217,22217S1c4
OP220602,22602S2_D2c6
OP210323,2021_03_24_0S5c2
OP240215,24215S2c1
OP230523,23523S3c4
OP231109,23n09S1c1
OP211123,2021_11_24_0S3c6
OP221024,22o24S3c7
OP230810,23810S1c8
OP220426,22427S2c3
OP220426,22427S4c4
OP221024,22o24S1c7
OP230817,23817S3c5
OP220623,22623S3c3
OP220111,22112S1_D2c1
OP220217,22217S3c5
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OP220127,22127S3_D2c7
OP231123,23n23S1_D2c5
OP240201,24201S2c8
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OP220308,22308S2c4
OP220127,22129S3_D2c8
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OP220518,22519S2c5
OP230808,23808S5c7
OP220914,22915S2c8
OP220127,22128S4c5
OP230314,23314S2_D2c7
OP240503,24503S3c2
OP220120,22121S1c5
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OP220602,22602S3_D2c7
OP220602,22602S1c3
OP230314,23314S4c3
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OP230314,23314S4c6
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OP210323,2021_03_24_0S3c1
OP230426,23426S3c2
OP211209,21d10S1c7
OP220111,22111S1c8
OP231130,23n30S1_D2c7
OP230810,23810S3c2
OP240503,24503S1_D2c4
OP220120,22121S1c4
OP220623,22623S4_D2c2
OP220623,22623S2c6
OP210615,2021_06_16_0S1_D2c1
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OP220602,22602S3c2
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OP240503,24503S1c1
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OP220602,22602S2c4
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OP230808,23808S2c4
OP220914,22915S2c1
OP210323,2021_03_25_0S4_D2c4
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OP220120,22121S1c7
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OP220426,22427S2c4
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OP240503,24503S2c7
OP240503,24503S2c8
OP220602,22602S2c1
OP221027,22o27S1c6
OP230817,23817S3_D2c1
OP231130,23n30S1_D2c5
OP220127,22127S2_D2c7
OP230808,23808S4c2
OP220127,22128S2c2
OP220602,22602S2c5
OP230817,23817S3c2
OP240117,24117S1c5
OP220518,22519S4c2
OP221020,22o21S4_D2c3
OP230420,23420S1c7
OP240201,24201S1c5
OP221027,22o27S1c3
OP230808,23808S3_D2c6
OP220308,22308S2c2
OP220120,22121S1c4
OP230209,23209S3c8
OP230209,23209S1_D2c2
OP221027,22o27S3_D2c7
OP201029,20o29S2c1
OP230808,23808S2_D2c4
OP220623,22623S3_D2c1
OP230314,23314S1c6
1 OP cell_ID
2 OP240201 24201S2c2
3 OP210323 2021_03_25_0S4_D2c6
4 OP230808 23808S2c6
5 OP240503 24503S1c6
6 OP230109 2311S3c2
7 OP211209 21d10S1_D2c7
8 OP220127 22128S3c5
9 OP221020 22o21S4_D2c6
10 OP230808 23808S1c1
11 OP210323 2021_03_25_0S4_D2c7
12 OP211209 21d10S4c3
13 OP240215 24215S2c5
14 OP220111 22112S5_D2c7
15 OP240321 24321S4c8
16 OP220623 22623S2c2
17 OP240221 24221S1c1
18 OP230209 23209S3_D2c6
19 OP240117 24117S2_D2c6
20 OP240201 24201S2c5
21 OP220518 22519S2c6
22 OP221024 22o24S1_D2c7
23 OP220426 22427S4c7
24 OP230523 23523S2c1
25 OP230808 23808S1_D2c6
26 OP211209 21d10S5c8
27 OP230817 23817S1c3
28 OP221027 22o27S1c2
29 OP210323 2021_03_25_0S6_D2c4
30 OP211123 2021_11_24_0S3c8
31 OP220217 22217S1c4
32 OP220602 22602S2_D2c6
33 OP210323 2021_03_24_0S5c2
34 OP240215 24215S2c1
35 OP230523 23523S3c4
36 OP231109 23n09S1c1
37 OP211123 2021_11_24_0S3c6
38 OP221024 22o24S3c7
39 OP230810 23810S1c8
40 OP220426 22427S2c3
41 OP220426 22427S4c4
42 OP221024 22o24S1c7
43 OP230817 23817S3c5
44 OP220623 22623S3c3
45 OP220111 22112S1_D2c1
46 OP220217 22217S3c5
47 OP220426 22427S2_D2c1
48 OP231123 23n23S1c5
49 OP220127 22127S3_D2c7
50 OP231123 23n23S1_D2c5
51 OP240201 24201S2c8
52 OP211123 2021_11_24_0S3c1
53 OP220308 22308S2c4
54 OP220127 22129S3_D2c8
55 OP211123 21n23S2c4
56 OP220518 22519S2c5
57 OP230808 23808S5c7
58 OP220914 22915S2c8
59 OP220127 22128S4c5
60 OP230314 23314S2_D2c7
61 OP240503 24503S3c2
62 OP220120 22121S1c5
63 OP221024 22o24S1c5
64 OP210615 2021_06_16_0S1_D2c3
65 OP221027 22o27S1c3
66 OP220602 22602S3_D2c7
67 OP220602 22602S1c3
68 OP230314 23314S4c3
69 OP240321 24321S3c1
70 OP230314 23314S4c6
71 OP220228 22228S2_D2c7
72 OP210323 2021_03_24_0S3c1
73 OP230426 23426S3c2
74 OP211209 21d10S1c7
75 OP220111 22111S1c8
76 OP231130 23n30S1_D2c7
77 OP230810 23810S3c2
78 OP240503 24503S1_D2c4
79 OP220120 22121S1c4
80 OP220623 22623S4_D2c2
81 OP220623 22623S2c6
82 OP210615 2021_06_16_0S1_D2c1
83 OP220518 22519S1c4
84 OP220602 22602S3c2
85 OP230523 23523S2c4
86 OP240503 24503S1c1
87 OP220217 22217S1c7
88 OP230523 23523S2c2
89 OP231130 23n30S2c5
90 OP231130 23n30S1_D2c6
91 OP240411 24411S1c5
92 OP220914 22915S2c7
93 OP220914 22915S3_D2c2
94 OP240503 24503S2c2
95 OP240417 24417S2_D2c1
96 OP220602 22602S2c4
97 OP220228 22228S1c6
98 OP220217 22218S2_D2c7
99 OP230808 23808S2c4
100 OP220914 22915S2c1
101 OP210323 2021_03_25_0S4_D2c4
102 OP230314 23314S3c1
103 OP220228 22228S2c1
104 OP220120 22121S1c7
105 OP230109 2311S1c1
106 OP230420 23420S2c1
107 OP220426 22427S2c4
108 OP220111 22112S6_D2c5
109 OP240503 24503S2c7
110 OP240503 24503S2c8
111 OP220602 22602S2c1
112 OP221027 22o27S1c6
113 OP230817 23817S3_D2c1
114 OP231130 23n30S1_D2c5
115 OP220127 22127S2_D2c7
116 OP230808 23808S4c2
117 OP220127 22128S2c2
118 OP220602 22602S2c5
119 OP230817 23817S3c2
120 OP240117 24117S1c5
121 OP220518 22519S4c2
122 OP221020 22o21S4_D2c3
123 OP230420 23420S1c7
124 OP240201 24201S1c5
125 OP221027 22o27S1c3
126 OP230808 23808S3_D2c6
127 OP220308 22308S2c2
128 OP220120 22121S1c4
129 OP230209 23209S3c8
130 OP230209 23209S1_D2c2
131 OP221027 22o27S3_D2c7
132 OP201029 20o29S2c1
133 OP230808 23808S2_D2c4
134 OP220623 22623S3_D2c1
135 OP230314 23314S1c6

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{
"cells": [
{
"cell_type": "markdown",
"id": "f11a76bf",
"metadata": {},
"source": [
"# Exercise: Add experiment information to electrophysiology data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b6f2742b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# Set some Pandas options: maximum number of rows/columns it's going to display\n",
"pd.set_option('display.max_rows', 1000)\n",
"pd.set_option('display.max_columns', 100)"
]
},
{
"cell_type": "markdown",
"id": "2967c84e",
"metadata": {},
"source": [
"# Load electrophysiology data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ed626ee3",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../../data/QC_passed_2024-07-04_collected.csv')\n",
"info = pd.read_csv('../../data/op_info.csv')"
]
},
{
"cell_type": "markdown",
"id": "2fef4d37",
"metadata": {},
"source": [
"# 1. Add experiment information to the electrophysiology results\n",
"\n",
"* Is there information for every experiment?\n",
"* How many experiments did each patcher perform? (i.e., individual OPs, or rows in `info`)\n",
"* How many samples did each patcher analyze? (i.e., individual rows in `df`)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f3f57eb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "44031178",
"metadata": {},
"source": [
"# 2. Remove outliers from the table\n",
"\n",
"1. Load the list of outliers in `outliers.csv`\n",
"2. Use an anti-join to remove the outliers from the table\n",
"3. How many samples (rows) are left in the data?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7fa953af",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "84270332",
"metadata": {},
"source": [
"# 3. Save final result in `processed_QC_passed_2024-07-04_collected_v1.csv`\n",
"\n",
"1. Using the `.to_csv` method of Pandas DataFrames"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7bcff45",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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