comparison gaiac_pm_data_pulling/Untitled.ipynb @ 3:2ae74925a4fe draft default tip

planemo upload for repository https://github.com/jaidevjoshi83/gaiac commit e9587f93346c7b55e1be00bad5844bf2db3ed03d-dirty
author jay
date Thu, 10 Jul 2025 19:40:59 +0000
parents
children
comparison
equal deleted inserted replaced
2:c69125e9fb3d 3:2ae74925a4fe
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "id": "4707b84c-b2f9-4740-bd62-a159e2c7e123",
7 "metadata": {},
8 "outputs": [],
9 "source": [
10 "import pandas as pd"
11 ]
12 },
13 {
14 "cell_type": "code",
15 "execution_count": 6,
16 "id": "c9870ed9-90cc-427b-b811-bd94c6b867c9",
17 "metadata": {},
18 "outputs": [],
19 "source": [
20 "a = [1,2,4,6]\n",
21 "b = [4,5,6,7]\n",
22 "\n",
23 "df = pd.DataFrame([a,b])"
24 ]
25 },
26 {
27 "cell_type": "code",
28 "execution_count": 8,
29 "id": "502e66d5-022d-47af-a299-a918f0ebd887",
30 "metadata": {},
31 "outputs": [
32 {
33 "data": {
34 "text/html": [
35 "<div>\n",
36 "<style scoped>\n",
37 " .dataframe tbody tr th:only-of-type {\n",
38 " vertical-align: middle;\n",
39 " }\n",
40 "\n",
41 " .dataframe tbody tr th {\n",
42 " vertical-align: top;\n",
43 " }\n",
44 "\n",
45 " .dataframe thead th {\n",
46 " text-align: right;\n",
47 " }\n",
48 "</style>\n",
49 "<table border=\"1\" class=\"dataframe\">\n",
50 " <thead>\n",
51 " <tr style=\"text-align: right;\">\n",
52 " <th></th>\n",
53 " <th>0</th>\n",
54 " <th>1</th>\n",
55 " </tr>\n",
56 " </thead>\n",
57 " <tbody>\n",
58 " <tr>\n",
59 " <th>0</th>\n",
60 " <td>1</td>\n",
61 " <td>4</td>\n",
62 " </tr>\n",
63 " <tr>\n",
64 " <th>1</th>\n",
65 " <td>2</td>\n",
66 " <td>5</td>\n",
67 " </tr>\n",
68 " <tr>\n",
69 " <th>2</th>\n",
70 " <td>4</td>\n",
71 " <td>6</td>\n",
72 " </tr>\n",
73 " <tr>\n",
74 " <th>3</th>\n",
75 " <td>6</td>\n",
76 " <td>7</td>\n",
77 " </tr>\n",
78 " </tbody>\n",
79 "</table>\n",
80 "</div>"
81 ],
82 "text/plain": [
83 " 0 1\n",
84 "0 1 4\n",
85 "1 2 5\n",
86 "2 4 6\n",
87 "3 6 7"
88 ]
89 },
90 "execution_count": 8,
91 "metadata": {},
92 "output_type": "execute_result"
93 }
94 ],
95 "source": [
96 "df.T"
97 ]
98 },
99 {
100 "cell_type": "code",
101 "execution_count": null,
102 "id": "f39e7015-3e8b-4123-8ec7-c05547ee28b1",
103 "metadata": {},
104 "outputs": [],
105 "source": []
106 }
107 ],
108 "metadata": {
109 "kernelspec": {
110 "display_name": "Python 3 (ipykernel)",
111 "language": "python",
112 "name": "python3"
113 },
114 "language_info": {
115 "codemirror_mode": {
116 "name": "ipython",
117 "version": 3
118 },
119 "file_extension": ".py",
120 "mimetype": "text/x-python",
121 "name": "python",
122 "nbconvert_exporter": "python",
123 "pygments_lexer": "ipython3",
124 "version": "3.10.14"
125 },
126 "widgets": {
127 "application/vnd.jupyter.widget-state+json": {
128 "state": {},
129 "version_major": 2,
130 "version_minor": 0
131 }
132 }
133 },
134 "nbformat": 4,
135 "nbformat_minor": 5
136 }