1167 lines
42 KiB
Plaintext
1167 lines
42 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-01-13T01:30:08.643756Z",
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"start_time": "2025-01-13T01:30:06.177472Z"
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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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"import tushare as ts\n",
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"\n",
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"ts_pro = ts.pro_api(token=\"64ebff4fa679167600b905ee45dd88e76f3963c0ff39157f3f085f0e\")"
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],
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"outputs": [],
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||
"execution_count": 1
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||
},
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{
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"metadata": {
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||
"ExecuteTime": {
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"end_time": "2025-01-13T01:30:08.652416Z",
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"start_time": "2025-01-13T01:30:08.646084Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def get_balance_sheet_df(start_year, end_year):\n",
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" result = ts_pro.balancesheet_vip(period=f\"{start_year}1231\")\n",
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" print(f\"Pull balance sheet: {start_year}\")\n",
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" for year in range(start_year + 1, end_year + 1):\n",
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" print(f\"Pull balance sheet: {year}\")\n",
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" period = f\"{year}1231\"\n",
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" temp = ts_pro.balancesheet_vip(period=period)\n",
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" result = pd.concat([result, temp], ignore_index=True)\n",
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" return result\n",
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"\n",
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"\n",
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"def get_income_df(start_year, end_year):\n",
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" result = ts_pro.income_vip(period=f\"{start_year}1231\")\n",
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" print(f\"Pull income: {start_year}\")\n",
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" for year in range(start_year + 1, end_year + 1):\n",
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" print(f\"Pull income: {year}\")\n",
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" period = f\"{year}1231\"\n",
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" temp = ts_pro.income_vip(period=period)\n",
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" result = pd.concat([result, temp], ignore_index=True)\n",
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" return result\n",
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"\n",
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"\n",
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"def get_cash_flow_df(start_year, end_year):\n",
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" result = ts_pro.cashflow_vip(period=f\"{start_year}1231\")\n",
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" print(f\"Pull cash flow: {start_year}\")\n",
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" for year in range(start_year + 1, end_year + 1):\n",
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" print(f\"Pull cash flow: {year}\")\n",
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" period = f\"{year}1231\"\n",
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" temp = ts_pro.cashflow_vip(period=period)\n",
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" result = pd.concat([result, temp], ignore_index=True)\n",
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" return result\n",
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"\n",
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"\n",
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"def clean_df(df):\n",
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" df = df.drop_duplicates(subset=[\"ts_code\", \"end_date\"])\n",
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" df[\"end_date\"] = df[\"end_date\"].str[:4]\n",
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" return df"
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],
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"id": "14a28ff4952f0df8",
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"outputs": [],
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"execution_count": 2
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||
},
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{
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||
"metadata": {
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"ExecuteTime": {
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"end_time": "2025-01-13T09:08:04.607819Z",
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"start_time": "2025-01-13T09:08:04.602640Z"
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}
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},
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"cell_type": "code",
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"source": [
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"start_year = 1990\n",
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"end_year = 2024"
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],
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"id": "dc68cde196159626",
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"outputs": [],
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||
"execution_count": 11
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||
},
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{
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-01-13T09:09:36.004427Z",
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||
"start_time": "2025-01-13T09:08:33.797018Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 财务负债表\n",
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"balance_sheet_df = clean_df(get_balance_sheet_df(start_year, end_year))\n",
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"balance_sheet_df.to_csv(f\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/财务报表/资产负债表{start_year}-{end_year}.csv\", index=False)"
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],
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||
"id": "33cd797a12ad567e",
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||
"outputs": [
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||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
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"Pull balance sheet: 1990\n",
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"Pull balance sheet: 1991\n",
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"Pull balance sheet: 1992\n"
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]
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},
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||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_50121/709533518.py:8: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
|
||
" result = pd.concat([result, temp], ignore_index=True)\n"
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||
]
|
||
},
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||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
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||
"Pull balance sheet: 1993\n",
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||
"Pull balance sheet: 1994\n",
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||
"Pull balance sheet: 1995\n",
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||
"Pull balance sheet: 1996\n",
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||
"Pull balance sheet: 1997\n",
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||
"Pull balance sheet: 1998\n",
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||
"Pull balance sheet: 1999\n",
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||
"Pull balance sheet: 2000\n",
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||
"Pull balance sheet: 2001\n",
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||
"Pull balance sheet: 2002\n",
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||
"Pull balance sheet: 2003\n",
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||
"Pull balance sheet: 2004\n",
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||
"Pull balance sheet: 2005\n",
|
||
"Pull balance sheet: 2006\n",
|
||
"Pull balance sheet: 2007\n",
|
||
"Pull balance sheet: 2008\n",
|
||
"Pull balance sheet: 2009\n",
|
||
"Pull balance sheet: 2010\n",
|
||
"Pull balance sheet: 2011\n",
|
||
"Pull balance sheet: 2012\n",
|
||
"Pull balance sheet: 2013\n",
|
||
"Pull balance sheet: 2014\n",
|
||
"Pull balance sheet: 2015\n",
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||
"Pull balance sheet: 2016\n",
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||
"Pull balance sheet: 2017\n",
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||
"Pull balance sheet: 2018\n",
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||
"Pull balance sheet: 2019\n",
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||
"Pull balance sheet: 2020\n",
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||
"Pull balance sheet: 2021\n",
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||
"Pull balance sheet: 2022\n",
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||
"Pull balance sheet: 2023\n",
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||
"Pull balance sheet: 2024\n"
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||
]
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||
},
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||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
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"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_50121/709533518.py:8: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
|
||
" result = pd.concat([result, temp], ignore_index=True)\n"
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||
]
|
||
}
|
||
],
|
||
"execution_count": 12
|
||
},
|
||
{
|
||
"metadata": {
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||
"ExecuteTime": {
|
||
"end_time": "2025-01-13T09:11:33.507055Z",
|
||
"start_time": "2025-01-13T09:10:33.447304Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"income_df = clean_df(get_income_df(start_year, end_year))\n",
|
||
"income_df.to_csv(f\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/财务报表/利润表{start_year}-{end_year}.csv\", index=False)"
|
||
],
|
||
"id": "17306c1524f5e173",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Pull income: 1990\n",
|
||
"Pull income: 1991\n",
|
||
"Pull income: 1992\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_50121/709533518.py:19: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
|
||
" result = pd.concat([result, temp], ignore_index=True)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Pull income: 1993\n",
|
||
"Pull income: 1994\n",
|
||
"Pull income: 1995\n",
|
||
"Pull income: 1996\n",
|
||
"Pull income: 1997\n",
|
||
"Pull income: 1998\n",
|
||
"Pull income: 1999\n",
|
||
"Pull income: 2000\n",
|
||
"Pull income: 2001\n",
|
||
"Pull income: 2002\n",
|
||
"Pull income: 2003\n",
|
||
"Pull income: 2004\n",
|
||
"Pull income: 2005\n",
|
||
"Pull income: 2006\n",
|
||
"Pull income: 2007\n",
|
||
"Pull income: 2008\n",
|
||
"Pull income: 2009\n",
|
||
"Pull income: 2010\n",
|
||
"Pull income: 2011\n",
|
||
"Pull income: 2012\n",
|
||
"Pull income: 2013\n",
|
||
"Pull income: 2014\n",
|
||
"Pull income: 2015\n",
|
||
"Pull income: 2016\n",
|
||
"Pull income: 2017\n",
|
||
"Pull income: 2018\n",
|
||
"Pull income: 2019\n",
|
||
"Pull income: 2020\n",
|
||
"Pull income: 2021\n",
|
||
"Pull income: 2022\n",
|
||
"Pull income: 2023\n",
|
||
"Pull income: 2024\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_50121/709533518.py:19: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
|
||
" result = pd.concat([result, temp], ignore_index=True)\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 13
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-01-13T09:13:32.890801Z",
|
||
"start_time": "2025-01-13T09:12:46.032342Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"cash_flow_df = clean_df(get_cash_flow_df(start_year, end_year))\n",
|
||
"cash_flow_df.to_csv(f\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/财务报表/现金流量表{start_year}-{end_year}.csv\", index=False)"
|
||
],
|
||
"id": "334dbe20f2047a1e",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Pull cash flow: 1990\n",
|
||
"Pull cash flow: 1991\n",
|
||
"Pull cash flow: 1992\n",
|
||
"Pull cash flow: 1993\n",
|
||
"Pull cash flow: 1994\n",
|
||
"Pull cash flow: 1995\n",
|
||
"Pull cash flow: 1996\n",
|
||
"Pull cash flow: 1997\n",
|
||
"Pull cash flow: 1998\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_50121/709533518.py:30: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
|
||
" result = pd.concat([result, temp], ignore_index=True)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Pull cash flow: 1999\n",
|
||
"Pull cash flow: 2000\n",
|
||
"Pull cash flow: 2001\n",
|
||
"Pull cash flow: 2002\n",
|
||
"Pull cash flow: 2003\n",
|
||
"Pull cash flow: 2004\n",
|
||
"Pull cash flow: 2005\n",
|
||
"Pull cash flow: 2006\n",
|
||
"Pull cash flow: 2007\n",
|
||
"Pull cash flow: 2008\n",
|
||
"Pull cash flow: 2009\n",
|
||
"Pull cash flow: 2010\n",
|
||
"Pull cash flow: 2011\n",
|
||
"Pull cash flow: 2012\n",
|
||
"Pull cash flow: 2013\n",
|
||
"Pull cash flow: 2014\n",
|
||
"Pull cash flow: 2015\n",
|
||
"Pull cash flow: 2016\n",
|
||
"Pull cash flow: 2017\n",
|
||
"Pull cash flow: 2018\n",
|
||
"Pull cash flow: 2019\n",
|
||
"Pull cash flow: 2020\n",
|
||
"Pull cash flow: 2021\n",
|
||
"Pull cash flow: 2022\n",
|
||
"Pull cash flow: 2023\n",
|
||
"Pull cash flow: 2024\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_50121/709533518.py:30: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
|
||
" result = pd.concat([result, temp], ignore_index=True)\n"
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||
]
|
||
}
|
||
],
|
||
"execution_count": 15
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-01-13T01:46:57.717599Z",
|
||
"start_time": "2025-01-13T01:46:46.377806Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"finance_df = pd.merge(balance_sheet_df, income_df, on=[\"ts_code\", \"end_date\"])\n",
|
||
"finance_df = pd.merge(finance_df, cash_flow_df, on=[\"ts_code\", \"end_date\"])\n",
|
||
"finance_df.to_csv(\"../temp/finance.csv\", index=False)"
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||
],
|
||
"id": "f8bea62f377b5e2",
|
||
"outputs": [],
|
||
"execution_count": 7
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-01-13T01:46:57.814028Z",
|
||
"start_time": "2025-01-13T01:46:57.728111Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "finance_df",
|
||
"id": "b14b477ca3c0f720",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" ts_code ann_date_x f_ann_date_x end_date report_type_x comp_type_x \\\n",
|
||
"0 830964.BJ 20180103 20180103 2014 1 1 \n",
|
||
"1 834765.BJ 20180105 20180105 2014 1 1 \n",
|
||
"2 835174.BJ 20180130 20180130 2014 1 1 \n",
|
||
"3 301076.SZ 20180117 20180117 2014 1 1 \n",
|
||
"4 601528.SH 20180116 20180116 2014 1 2 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"47434 603260.SH 20240430 20240430 2023 1 1 \n",
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"47435 603828.SH 20240430 20240430 2023 1 1 \n",
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"47436 002120.SZ 20240430 20240430 2023 1 1 \n",
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"47437 000790.SZ 20240430 20240430 2023 1 1 \n",
|
||
"47438 000504.SZ 20240430 20240430 2023 1 1 \n",
|
||
"\n",
|
||
" end_type_x total_share cap_rese undistr_porfit ... \\\n",
|
||
"0 4 6.800000e+07 1.949319e+07 5.757873e+06 ... \n",
|
||
"1 4 1.500000e+07 2.169516e+07 9.042014e+06 ... \n",
|
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"2 4 5.714286e+07 NaN -7.873967e+06 ... \n",
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||
"3 4 5.000000e+07 NaN 1.800787e+07 ... \n",
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"4 4 1.197900e+09 3.048310e+08 1.363431e+09 ... \n",
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"... ... ... ... ... ... \n",
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"47434 4 1.182207e+09 1.138407e+10 1.921573e+10 ... \n",
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"47435 4 5.959602e+08 2.395205e+08 -1.640928e+08 ... \n",
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"47436 4 2.899193e+09 2.810602e+09 1.234578e+10 ... \n",
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"47437 4 6.281426e+08 7.375118e+07 2.299767e+08 ... \n",
|
||
"47438 4 3.115739e+08 4.234729e+08 -5.311005e+08 ... \n",
|
||
"\n",
|
||
" net_dism_capital_add net_cash_rece_sec credit_impa_loss \\\n",
|
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"0 NaN NaN NaN \n",
|
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"2 NaN NaN NaN \n",
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"... ... ... ... \n",
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||
"\n",
|
||
" use_right_asset_dep oth_loss_asset end_bal_cash beg_bal_cash \\\n",
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||
"0 NaN None NaN NaN \n",
|
||
"1 NaN None NaN NaN \n",
|
||
"2 NaN None NaN NaN \n",
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"3 NaN None NaN NaN \n",
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"4 NaN None NaN NaN \n",
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"... ... ... ... ... \n",
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"47434 7.167836e+06 None 1.034290e+09 1.822382e+09 \n",
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"47435 4.186533e+06 None 1.255399e+08 1.020296e+08 \n",
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"47436 5.239484e+08 None 4.313758e+09 3.507770e+09 \n",
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"47437 2.214418e+06 None 1.164838e+08 3.787762e+07 \n",
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||
"47438 3.289952e+06 None 4.937811e+08 2.181183e+08 \n",
|
||
"\n",
|
||
" end_bal_cash_equ beg_bal_cash_equ update_flag \n",
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||
"0 NaN NaN 0 \n",
|
||
"1 NaN NaN 0 \n",
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||
"2 NaN NaN 0 \n",
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||
"3 NaN NaN 1 \n",
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"4 NaN NaN 1 \n",
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"... ... ... ... \n",
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||
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|
||
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|
||
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||
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||
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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||
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||
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|
||
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
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|
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|
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|
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|
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|
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|
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|
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"metadata": {
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|
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|
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|
||
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|
||
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|
||
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|
||
"source": [
|
||
"import akshare as ak\n",
|
||
"\n",
|
||
"# ak.stock_balance_sheet_by_yearly_em(\"SZ000001\")\n",
|
||
"# df = ak.stock_financial_report_sina(\"sz000001\", symbol=\"资产负债表\")\n",
|
||
"df = ak.stock_financial_benefit_ths(\"000001\")\n",
|
||
"# df[df[\"报告日\"].str.endswith(\"1231\")]\n",
|
||
"df"
|
||
],
|
||
"id": "582154dbc28b6bd",
|
||
"outputs": [
|
||
{
|
||
"data": {
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|
||
" 报告期 报表核心指标 *净利润 *营业总收入 *营业支出 *归属于母公司所有者的净利润 \\\n",
|
||
"0 2024-09-30 397.29亿 1115.82亿 637.13亿 397.29亿 \n",
|
||
"1 2024-06-30 258.79亿 771.32亿 450.45亿 258.79亿 \n",
|
||
"2 2024-03-31 149.32亿 387.70亿 202.16亿 149.32亿 \n",
|
||
"3 2023-12-31 464.55亿 1646.99亿 1067.71亿 464.55亿 \n",
|
||
"4 2023-09-30 396.35亿 1276.34亿 785.87亿 396.35亿 \n",
|
||
".. ... ... ... ... ... ... \n",
|
||
"110 1993-06-30 1.37亿 2.66亿 False 1.37亿 \n",
|
||
"111 1992-12-31 1.72亿 4.76亿 False 1.72亿 \n",
|
||
"112 1991-12-31 1.13亿 3.35亿 False 1.13亿 \n",
|
||
"113 1990-12-31 7087.50万 False False 7087.50万 \n",
|
||
"114 1989-12-31 4302.00万 False False 4302.00万 \n",
|
||
"\n",
|
||
" *扣除非经常性损益后的净利润 报表全部指标 一、营业总收入 其中：营业收入 ... 少数股东损益 扣除非经常性损益后的利润 六、每股收益 \\\n",
|
||
"0 397.48亿 1115.82亿 1115.82亿 ... False 397.48亿 \n",
|
||
"1 258.80亿 771.32亿 771.32亿 ... False 258.80亿 \n",
|
||
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|
||
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|
||
"4 395.68亿 1276.34亿 1276.34亿 ... False 395.68亿 \n",
|
||
".. ... ... ... ... ... ... ... ... \n",
|
||
"110 False 2.66亿 2.66亿 ... False False \n",
|
||
"111 False 4.76亿 4.76亿 ... False False \n",
|
||
"112 False 3.35亿 3.35亿 ... False False \n",
|
||
"113 False False False ... False False \n",
|
||
"114 False False False ... False False \n",
|
||
"\n",
|
||
" （一）基本每股收益 （二）稀释每股收益 七、其他综合收益 归属母公司所有者的其他综合收益 八、综合收益总额 归属于母公司股东的综合收益总额 \\\n",
|
||
"0 1.94 1.94 -7.86亿 -7.86亿 389.43亿 False \n",
|
||
"1 1.23 1.23 -3.56亿 -3.56亿 255.23亿 False \n",
|
||
"2 0.66 0.66 3.45亿 3.45亿 152.77亿 False \n",
|
||
"3 2.25 2.25 -3.72亿 -3.72亿 460.83亿 False \n",
|
||
"4 1.94 1.94 -8.38亿 -8.38亿 387.97亿 False \n",
|
||
".. ... ... ... ... ... ... \n",
|
||
"110 False False False False False False \n",
|
||
"111 False False False False False False \n",
|
||
"112 False False False False False False \n",
|
||
"113 False False False False False False \n",
|
||
"114 False False False False False False \n",
|
||
"\n",
|
||
" 归属于少数股东的综合收益总额 \n",
|
||
"0 False \n",
|
||
"1 False \n",
|
||
"2 False \n",
|
||
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||
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|
||
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||
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||
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||
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" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" 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>报告期</th>\n",
|
||
" <th>报表核心指标</th>\n",
|
||
" <th>*净利润</th>\n",
|
||
" <th>*营业总收入</th>\n",
|
||
" <th>*营业支出</th>\n",
|
||
" <th>*归属于母公司所有者的净利润</th>\n",
|
||
" <th>*扣除非经常性损益后的净利润</th>\n",
|
||
" <th>报表全部指标</th>\n",
|
||
" <th>一、营业总收入</th>\n",
|
||
" <th>其中：营业收入</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>少数股东损益</th>\n",
|
||
" <th>扣除非经常性损益后的利润</th>\n",
|
||
" <th>六、每股收益</th>\n",
|
||
" <th>（一）基本每股收益</th>\n",
|
||
" <th>（二）稀释每股收益</th>\n",
|
||
" <th>七、其他综合收益</th>\n",
|
||
" <th>归属母公司所有者的其他综合收益</th>\n",
|
||
" <th>八、综合收益总额</th>\n",
|
||
" <th>归属于母公司股东的综合收益总额</th>\n",
|
||
" <th>归属于少数股东的综合收益总额</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2024-09-30</td>\n",
|
||
" <td></td>\n",
|
||
" <td>397.29亿</td>\n",
|
||
" <td>1115.82亿</td>\n",
|
||
" <td>637.13亿</td>\n",
|
||
" <td>397.29亿</td>\n",
|
||
" <td>397.48亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1115.82亿</td>\n",
|
||
" <td>1115.82亿</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>397.48亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1.94</td>\n",
|
||
" <td>1.94</td>\n",
|
||
" <td>-7.86亿</td>\n",
|
||
" <td>-7.86亿</td>\n",
|
||
" <td>389.43亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2024-06-30</td>\n",
|
||
" <td></td>\n",
|
||
" <td>258.79亿</td>\n",
|
||
" <td>771.32亿</td>\n",
|
||
" <td>450.45亿</td>\n",
|
||
" <td>258.79亿</td>\n",
|
||
" <td>258.80亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>771.32亿</td>\n",
|
||
" <td>771.32亿</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>258.80亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1.23</td>\n",
|
||
" <td>1.23</td>\n",
|
||
" <td>-3.56亿</td>\n",
|
||
" <td>-3.56亿</td>\n",
|
||
" <td>255.23亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2024-03-31</td>\n",
|
||
" <td></td>\n",
|
||
" <td>149.32亿</td>\n",
|
||
" <td>387.70亿</td>\n",
|
||
" <td>202.16亿</td>\n",
|
||
" <td>149.32亿</td>\n",
|
||
" <td>149.06亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>387.70亿</td>\n",
|
||
" <td>387.70亿</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>149.06亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>0.66</td>\n",
|
||
" <td>0.66</td>\n",
|
||
" <td>3.45亿</td>\n",
|
||
" <td>3.45亿</td>\n",
|
||
" <td>152.77亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2023-12-31</td>\n",
|
||
" <td></td>\n",
|
||
" <td>464.55亿</td>\n",
|
||
" <td>1646.99亿</td>\n",
|
||
" <td>1067.71亿</td>\n",
|
||
" <td>464.55亿</td>\n",
|
||
" <td>464.31亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1646.99亿</td>\n",
|
||
" <td>1646.99亿</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>464.31亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>2.25</td>\n",
|
||
" <td>2.25</td>\n",
|
||
" <td>-3.72亿</td>\n",
|
||
" <td>-3.72亿</td>\n",
|
||
" <td>460.83亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2023-09-30</td>\n",
|
||
" <td></td>\n",
|
||
" <td>396.35亿</td>\n",
|
||
" <td>1276.34亿</td>\n",
|
||
" <td>785.87亿</td>\n",
|
||
" <td>396.35亿</td>\n",
|
||
" <td>395.68亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1276.34亿</td>\n",
|
||
" <td>1276.34亿</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>395.68亿</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1.94</td>\n",
|
||
" <td>1.94</td>\n",
|
||
" <td>-8.38亿</td>\n",
|
||
" <td>-8.38亿</td>\n",
|
||
" <td>387.97亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>110</th>\n",
|
||
" <td>1993-06-30</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1.37亿</td>\n",
|
||
" <td>2.66亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.37亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>2.66亿</td>\n",
|
||
" <td>2.66亿</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>111</th>\n",
|
||
" <td>1992-12-31</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1.72亿</td>\n",
|
||
" <td>4.76亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.72亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>4.76亿</td>\n",
|
||
" <td>4.76亿</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>112</th>\n",
|
||
" <td>1991-12-31</td>\n",
|
||
" <td></td>\n",
|
||
" <td>1.13亿</td>\n",
|
||
" <td>3.35亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.13亿</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>3.35亿</td>\n",
|
||
" <td>3.35亿</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>113</th>\n",
|
||
" <td>1990-12-31</td>\n",
|
||
" <td></td>\n",
|
||
" <td>7087.50万</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>7087.50万</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>114</th>\n",
|
||
" <td>1989-12-31</td>\n",
|
||
" <td></td>\n",
|
||
" <td>4302.00万</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>4302.00万</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>115 rows × 43 columns</p>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 21
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|