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add resample
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‎Chapter3/date_time.ipynb‎

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@@ -107,7 +107,24 @@
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@@ -149,7 +166,24 @@
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@@ -476,7 +510,24 @@
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"application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 10;\n var nbb_unformatted_code = \"df.rolling(3).mean()\";\n var nbb_formatted_code = \"df.rolling(3).mean()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ",
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@@ -565,7 +616,24 @@
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"application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 6;\n var nbb_unformatted_code = \"df['date'].dt.time\";\n var nbb_formatted_code = \"df[\\\"date\\\"].dt.time\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ",
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@@ -742,7 +810,24 @@
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"application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 28;\n var nbb_unformatted_code = \"df.loc['2019':]\";\n var nbb_formatted_code = \"df.loc[\\\"2019\\\":]\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ",
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@@ -835,7 +920,24 @@
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"application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 31;\n var nbb_unformatted_code = \"# Get dates ranging from 2021/7/20 to 2021/7/25\\nnew_index = pd.date_range('2021-07-20', '2021-07-25')\\n\\n# Conform Series to new index\\nnew_s = s.reindex(new_index, fill_value=0)\\nnew_s \";\n var nbb_formatted_code = \"# Get dates ranging from 2021/7/20 to 2021/7/25\\nnew_index = pd.date_range(\\\"2021-07-20\\\", \\\"2021-07-25\\\")\\n\\n# Conform Series to new index\\nnew_s = s.reindex(new_index, fill_value=0)\\nnew_s\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ",
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" var nbb_unformatted_code = \"# Get dates ranging from 2021/7/20 to 2021/7/25\\nnew_index = pd.date_range('2021-07-20', '2021-07-25')\\n\\n# Conform Series to new index\\nnew_s = s.reindex(new_index, fill_value=0)\\nnew_s \";\n",
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" var nbb_formatted_code = \"# Get dates ranging from 2021/7/20 to 2021/7/25\\nnew_index = pd.date_range(\\\"2021-07-20\\\", \\\"2021-07-25\\\")\\n\\n# Conform Series to new index\\nnew_s = s.reindex(new_index, fill_value=0)\\nnew_s\";\n",
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@@ -1027,7 +1129,24 @@
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"application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 33;\n var nbb_unformatted_code = \"filtered_df = df[df.date <= \\\"2021-07-21\\\"]\\nfiltered_df\";\n var nbb_formatted_code = \"filtered_df = df[df.date <= \\\"2021-07-21\\\"]\\nfiltered_df\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ",
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" setTimeout(function() {\n",
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" var nbb_unformatted_code = \"filtered_df = df[df.date <= \\\"2021-07-21\\\"]\\nfiltered_df\";\n",
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@@ -1040,11 +1159,93 @@
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"filtered_df = df[df.date <= \"2021-07-21\"]\n",
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"filtered_df"
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},
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{
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"cell_type": "markdown",
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"id": "b8fe252a",
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"metadata": {},
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"source": [
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"### resample: Resample Time-Series Data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d02e971a",
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"metadata": {},
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"source": [
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"If you want to change the frequency of time-series data, use `resample`. In the code below, I use `resample` to show the records every two days instead of every day. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "7a17658f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-02-10T15:31:00.856020Z",
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"start_time": "2022-02-10T15:31:00.837008Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2022-02-01 1\n",
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"2022-02-02 1\n",
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"2022-02-03 0\n",
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"2022-02-04 2\n",
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"2022-02-05 4\n",
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"2022-02-06 9\n",
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"Freq: D, dtype: int64"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd \n",
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"from numpy.random import randint\n",
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"\n",
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"index = pd.date_range(\"2022-02-01\", \"2022-02-6\")\n",
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"s = pd.Series(index=index, data=randint(0, 10, 6))\n",
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"s "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "97a546dc",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-02-10T15:31:04.676673Z",
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"start_time": "2022-02-10T15:31:04.660037Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2022-02-01 2\n",
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"2022-02-03 2\n",
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"2022-02-05 13\n",
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"Freq: 2D, dtype: int64"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"s.resample('2D').sum()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},

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