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SDP_ml/test.ipynb
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{ | |
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"<div>\n", | |
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"text/plain": [ | |
" A B\n", | |
"0 1 4\n", | |
"1 2 3\n", | |
"2 3 2\n", | |
"3 4 1" | |
] | |
}, | |
"execution_count": 1, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"import pandas as pd\n", | |
"df = pd.DataFrame({\"A\":[1,2,3,4],\"B\":[4,3,2,1]})\n", | |
"df" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [ | |
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"data": { | |
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"<div>\n", | |
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"\n", | |
" .dataframe tbody tr th {\n", | |
" vertical-align: top;\n", | |
" }\n", | |
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" text-align: right;\n", | |
" }\n", | |
"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>A</th>\n", | |
" <th>B</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>1</td>\n", | |
" <td>4</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>2</td>\n", | |
" <td>3</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>3</td>\n", | |
" <td>2</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>4</td>\n", | |
" <td>1</td>\n", | |
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"text/plain": [ | |
" A B\n", | |
"0 1 4\n", | |
"1 2 3\n", | |
"2 3 2\n", | |
"3 4 1" | |
] | |
}, | |
"execution_count": 10, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df[\"B\"].apply(lambda i:i*2)\n", | |
"df" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 28, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"<class 'pandas.core.series.Series'>\n", | |
"<class 'pandas.core.series.Series'>\n", | |
"<class 'pandas.core.series.Series'>\n", | |
"<class 'pandas.core.series.Series'>\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"0 5\n", | |
"1 5\n", | |
"2 5\n", | |
"3 5\n", | |
"dtype: int64" | |
] | |
}, | |
"execution_count": 28, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"import numpy as np\n", | |
"def cal_row(row):\n", | |
" i = row.iloc[0]\n", | |
" j = row.iloc[1]\n", | |
" print(type(row))\n", | |
" return i + j\n", | |
"\n", | |
"df.apply(cal_row,axis = 1)\n" | |
] | |
}, | |
{ | |
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"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "py310", | |
"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": "3.10.13" | |
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"nbformat": 4, | |
"nbformat_minor": 2 | |
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