FANBOYANG/Numpy_learning
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add cut and split

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FANBOYANG
2022-11-05 15:18:14 +08:00
parent 8e3d535391
commit 1ff85cfa8b
3 changed files with 153 additions and 11 deletions
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1
README.md
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@@ -3,3 +3,4 @@ This repo is a note of numpy leaning
## Content
[random](./random.ipynb)
[cut and split](./cut_split.ipynb)

121
cut_split.ipynb Normal file
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@@ -0,0 +1,121 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 数据拼接\n",
"\n",
"1. np.concatenate 是numpy中对array进行拼接的函数\n",
"axis参数为指定按照哪个维度进行拼接"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 2.62434536 0.38824359 0.47182825 -0.07296862]\n",
" [ 1.86540763 -1.3015387 2.74481176 0.2387931 ]\n",
" [ 1.3190391 0.75062962 2.46210794 -1.06014071]\n",
" [ 0.6775828 0.61594565 2.13376944 -0.09989127]\n",
" [ 0.82757179 0.12214158 1.04221375 1.58281521]] \n",
" (5, 4) \n",
"\n",
"[[-0.10061918 2.14472371 1.90159072 1.50249434]\n",
" [ 1.90085595 0.31627214 0.87710977 0.06423057]\n",
" [ 0.73211192 1.53035547 0.30833925 0.60324647]] \n",
" (3, 4) \n",
"\n",
"[[ 0.3128273 0.15479436]\n",
" [ 0.32875387 0.9873354 ]\n",
" [-0.11731035 1.2344157 ]\n",
" [ 2.65980218 1.74204416]\n",
" [ 0.80816445 0.11237104]] \n",
" (5, 2) \n",
"\n",
"[[ 2.62434536 0.38824359 0.47182825 -0.07296862]\n",
" [ 1.86540763 -1.3015387 2.74481176 0.2387931 ]\n",
" [ 1.3190391 0.75062962 2.46210794 -1.06014071]\n",
" [ 0.6775828 0.61594565 2.13376944 -0.09989127]\n",
" [ 0.82757179 0.12214158 1.04221375 1.58281521]\n",
" [-0.10061918 2.14472371 1.90159072 1.50249434]\n",
" [ 1.90085595 0.31627214 0.87710977 0.06423057]\n",
" [ 0.73211192 1.53035547 0.30833925 0.60324647]] \n",
" (8, 4) \n",
"\n",
"[[ 2.62434536 0.38824359 0.47182825 -0.07296862 0.3128273 0.15479436]\n",
" [ 1.86540763 -1.3015387 2.74481176 0.2387931 0.32875387 0.9873354 ]\n",
" [ 1.3190391 0.75062962 2.46210794 -1.06014071 -0.11731035 1.2344157 ]\n",
" [ 0.6775828 0.61594565 2.13376944 -0.09989127 2.65980218 1.74204416]\n",
" [ 0.82757179 0.12214158 1.04221375 1.58281521 0.80816445 0.11237104]] \n",
" (5, 6) \n",
"\n"
]
}
],
"source": [
"rdm = np.random.RandomState(1)\n",
"x1 = rdm.normal(1,1,(5,4))\n",
"x2 = rdm.normal(1,1,(3,4))\n",
"x3 = rdm.normal(1,1,(5,2))\n",
"print(x1,\"\\n\",x1.shape,\"\\n\")\n",
"print(x2,\"\\n\",x2.shape,\"\\n\")\n",
"print(x3,\"\\n\",x3.shape,\"\\n\")\n",
"\n",
"con1 = np.concatenate([x1,x2],axis=0)\n",
"print(con1,\"\\n\",con1.shape,\"\\n\")\n",
"\n",
"con2 = np.concatenate([x1,x3],axis=1)\n",
"print(con2,\"\\n\",con2.shape,\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.13 ('gym')",
"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": "3.9.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "eb62451c918ef6a4174992a3510c06ea27ba0bc2fc5ee7a9f470b6f52b0b170f"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

40
random.ipynb
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@@ -134,12 +134,22 @@
"功能： 生成[0,1)之间的浮点数，通过size参数来指定维数。\n",
"说明：\n",
"size : int or tuple of ints, optional.\n",
"Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. "
"Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. \n",
"\n",
"4. numpy.random.rand()\n",
"均匀分布\n",
"范围 [0, 1)\n",
"\n",
"5. numpy.random.normal(loc=mu, scale=sigma, size)\n",
"正态分布\n",
"mu，均值\n",
"sigma，标准差\n",
"size，数据shape，默认一个值"
]
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -148,17 +158,21 @@
"text": [
"[0 0 0 0] \n",
"\n",
"[[ 0 -2 0]\n",
" [ 1 -1 -1]] \n",
"[[ 2 1 0]\n",
" [-2 0 0]] \n",
"\n",
"0.0940234518052504 \n",
"0.9888926077885405 \n",
"\n",
"[0.58264778] \n",
"[0.32897483] \n",
"\n",
"[0.19358132 0.3565561 ] \n",
"[0.96680953 0.76946094] \n",
"\n",
"[[0.32759701 0.74016873 0.83999783]\n",
" [0.62572959 0.13477642 0.86937912]] \n",
"[[0.0936316 0.2776172 0.16938396]\n",
" [0.21494328 0.26245945 0.22463559]] \n",
"\n",
"[0.36292738 0.51118156 0.35250669] \n",
"\n",
"[[ 0.07101816 0.1111697 -0.89099377]] \n",
"\n"
]
}
@@ -176,7 +190,13 @@
"z3 = np.random.random(2) #生成1×2的数组\n",
"print(z3,\"\\n\")\n",
"z4 = np.random.random((2,3)) #生成一个2行3列的数组\n",
"print(z4,\"\\n\")"
"print(z4,\"\\n\")\n",
"\n",
"z5 = np.random.rand(3) # 生成1×3的数组\n",
"print(z5,\"\\n\")\n",
"\n",
"z6 = np.random.normal(0,1,(1,3))\n",
"print(z6,\"\\n\")"
]
}
],