726 lines
25 KiB
Plaintext
726 lines
25 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": "2025-01-11T03:30:00.044697Z",
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"start_time": "2025-01-11T03:30:00.041372Z"
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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": 25
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-01-09T06:28:32.524630Z",
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"start_time": "2025-01-09T06:27:15.688831Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 从1989年开始循环年份\n",
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"for year in range(2000, 2024):\n",
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" # ts_pro.balancesheet_vip(period=f\"{year}1231\").to_csv(f\"../temp/财务报表/资产负债表/{year}.csv\")\n",
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" # ts_pro.cashflow_vip(period=f\"{year}1231\").to_csv(f\"../temp/财务报表/现金流量表/{year}.csv\")\n",
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" # ts_pro.income_vip(period=f\"{year}1231\").to_csv(f\"../temp/财务报表/利润表/{year}.csv\")\n",
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" ts_pro.fina_indicator_vip(period=f\"{year}1231\").to_csv(f\"../temp/财务报表/财务指标/{year}.csv\")\n",
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" # ts_pro.fina_audit(period=f\"{year}1231\").to_csv(f\"../temp/财务报表/审计意见/{year}.csv\")"
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],
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"id": "b03b118c70b21315",
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"outputs": [],
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"execution_count": 15
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-01-10T03:01:43.076024Z",
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"start_time": "2025-01-10T03:01:42.644700Z"
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}
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},
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"cell_type": "code",
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"source": [
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"df_income = ts_pro.income(ts_code=\"600519.SH\", period=\"20231231\", fields=\"ts_code,end_date,n_income\")\n",
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"df_balancesheet = ts_pro.balancesheet(ts_code=\"600519.SH\", period=\"20231231\",\n",
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" fields=\"ts_code,end_date,total_hldr_eqy_exc_min_int\")\n",
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"merged_df = pd.merge(df_income, df_balancesheet, on=['ts_code', 'end_date'])\n",
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"merged_df['roe'] = merged_df['n_income'] / merged_df['total_hldr_eqy_exc_min_int']\n",
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"merged_df"
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],
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"id": "575590399bf15e70",
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"outputs": [
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{
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"data": {
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"text/plain": [
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" ts_code end_date n_income total_hldr_eqy_exc_min_int roe\n",
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"0 600519.SH 20231231 7.752148e+10 2.156686e+11 0.359447"
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],
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>ts_code</th>\n",
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" <th>end_date</th>\n",
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" <th>n_income</th>\n",
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" <th>total_hldr_eqy_exc_min_int</th>\n",
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" <th>roe</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>600519.SH</td>\n",
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" <td>20231231</td>\n",
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" <td>7.752148e+10</td>\n",
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" <td>2.156686e+11</td>\n",
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" <td>0.359447</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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]
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},
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"execution_count": 31,
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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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"execution_count": 31
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-01-10T02:59:32.764968Z",
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"start_time": "2025-01-10T02:59:32.601657Z"
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}
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},
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"cell_type": "code",
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"source": "ts_pro.fina_indicator(ts_code=\"600519.SH\", period=\"20231231\")[\"roe\"]",
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"id": "d59f62a613379fbd",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0 36.1755\n",
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"Name: roe, dtype: float64"
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]
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},
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"execution_count": 26,
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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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"execution_count": 26
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-01-10T04:01:33.511246Z",
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"start_time": "2025-01-10T04:01:33.382955Z"
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}
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},
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"cell_type": "code",
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"source": "ts_pro.income(ts_code='600519.SH', start_date='20220101', end_date='20241231', fields='end_date,n_income')",
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"id": "c43925cfb5552cd9",
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"outputs": [
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{
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"data": {
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"text/plain": [
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" end_date n_income\n",
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"0 20240930 6.303146e+10\n",
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"1 20240630 4.317691e+10\n",
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"2 20240331 2.488235e+10\n",
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"3 20231231 7.752148e+10\n",
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"4 20230930 5.482717e+10\n",
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"5 20230630 3.733197e+10\n",
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"6 20230331 2.152473e+10\n",
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"7 20221231 6.537514e+10\n",
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"8 20221231 6.537514e+10\n",
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"9 20220930 4.631399e+10\n",
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"10 20220630 3.110536e+10\n",
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"11 20220331 1.795212e+10\n",
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"12 20211231 5.572053e+10\n",
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"13 20211231 5.572053e+10"
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],
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>end_date</th>\n",
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" <th>n_income</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>20240930</td>\n",
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" <td>6.303146e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>20240630</td>\n",
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" <td>4.317691e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>20240331</td>\n",
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" <td>2.488235e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>20231231</td>\n",
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" <td>7.752148e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>20230930</td>\n",
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" <td>5.482717e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>20230630</td>\n",
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" <td>3.733197e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>20230331</td>\n",
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" <td>2.152473e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>20221231</td>\n",
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" <td>6.537514e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>20221231</td>\n",
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" <td>6.537514e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>20220930</td>\n",
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" <td>4.631399e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>20220630</td>\n",
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" <td>3.110536e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>20220331</td>\n",
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" <td>1.795212e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>20211231</td>\n",
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" <td>5.572053e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>20211231</td>\n",
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" <td>5.572053e+10</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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||
]
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},
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"execution_count": 40,
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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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"execution_count": 40
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||
},
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{
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"metadata": {
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"ExecuteTime": {
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||
"end_time": "2025-01-10T04:17:56.196027Z",
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"start_time": "2025-01-10T04:17:55.879469Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 获取净利润\n",
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"income_df = ts_pro.income(ts_code='600519.SH', start_date='20220101', end_date='20241231', fields='end_date,n_income')\n",
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"net_profit = income_df[income_df['end_date'] == '20231231']['n_income'].values[0]\n",
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"\n",
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"# 获取期初和期末的股东权益和总资产\n",
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"balancesheet_df = ts_pro.balancesheet(ts_code='600519.SH', start_date='20220101', end_date='20241231',\n",
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" fields='end_date,total_hldr_eqy_exc_min_int,total_assets')\n",
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"eqy_initial = balancesheet_df[balancesheet_df['end_date'] == '20230331']['total_hldr_eqy_exc_min_int'].values[0]\n",
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"eqy_final = balancesheet_df[balancesheet_df['end_date'] == '20231231']['total_hldr_eqy_exc_min_int'].values[0]\n",
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"total_assets_initial = balancesheet_df[balancesheet_df['end_date'] == '20230331']['total_assets'].values[0]\n",
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"total_assets_final = balancesheet_df[balancesheet_df['end_date'] == '20231231']['total_assets'].values[0]\n",
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"\n",
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"# 计算平均股东权益和平均总资产\n",
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"avg_eqy = (eqy_initial + eqy_final) / 2\n",
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"avg_assets = (total_assets_initial + total_assets_final) / 2\n",
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"\n",
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"# 计算ROE和ROA\n",
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"roe = net_profit / avg_eqy\n",
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"roa = net_profit / avg_assets\n",
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"\n",
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"print(f'ROE: {roe:.4f}')\n",
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"print(f'ROA: {roa:.4f}')"
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],
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"id": "caf1a1555d4c972e",
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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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"ROE: 0.3573\n",
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"ROA: 0.2919\n"
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]
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}
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],
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"execution_count": 43
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},
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{
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"metadata": {
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"ExecuteTime": {
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||
"end_time": "2025-01-10T04:19:57.750533Z",
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"start_time": "2025-01-10T04:19:57.531524Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 获取营业收入净额\n",
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"income_df = ts_pro.income(ts_code='600519.SH', start_date='20220101', end_date='20241231', fields='end_date,revenue')\n",
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"revenue = income_df[income_df['end_date'] == '20231231']['revenue'].values[0]\n",
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"\n",
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"# 获取期初（上一年12月31日）和期末（当前年12月31日）的总资产\n",
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"balancesheet_df = ts_pro.balancesheet(ts_code='600519.SH', start_date='20220101', end_date='20241231',\n",
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" fields='end_date,total_assets')\n",
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"total_assets_initial = balancesheet_df[balancesheet_df['end_date'] == '20221231']['total_assets'].values[0]\n",
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"total_assets_final = balancesheet_df[balancesheet_df['end_date'] == '20231231']['total_assets'].values[0]\n",
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"\n",
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"# 计算平均总资产\n",
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"avg_assets = (total_assets_initial + total_assets_final) / 2\n",
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"\n",
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"# 计算总资产周转率\n",
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"total_asset_turnover = revenue / avg_assets\n",
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"\n",
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"print(f'总资产周转率: {total_asset_turnover:.4f}')"
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],
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"id": "49488c6640beb4ed",
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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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"总资产周转率: 0.5604\n"
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]
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}
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],
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"execution_count": 48
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||
},
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{
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-01-10T04:23:50.762070Z",
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"start_time": "2025-01-10T04:23:50.585818Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 获取期末的现金及现金等价物和总资产\n",
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"balancesheet_df = ts_pro.balancesheet(ts_code='600519.SH', start_date='20220101', end_date='20241231',\n",
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" fields='end_date, money_cap, total_assets')\n",
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"cash_and_equivalents = balancesheet_df[balancesheet_df['end_date'] == '20231231']['money_cap'].values[0]\n",
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"total_assets = balancesheet_df[balancesheet_df['end_date'] == '20231231']['total_assets'].values[0]\n",
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"\n",
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"# 计算现金和现金等价物总和占总资产的比率\n",
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"cash_ratio = cash_and_equivalents / total_assets\n",
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"\n",
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"print(f'现金和现金等价物总和占总资产的比率: {cash_ratio:.4f}')"
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],
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"id": "526ef46373e1c8fe",
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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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"现金和现金等价物总和占总资产的比率: 0.2533\n"
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]
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}
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],
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"execution_count": 49
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||
},
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{
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"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-01-10T04:27:11.461667Z",
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||
"start_time": "2025-01-10T04:27:11.160878Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 获取营业收入\n",
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"income_df = ts_pro.income(ts_code='600519.SH', start_date='20220101', end_date='20241231', fields='end_date, revenue')\n",
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"revenue = income_df[income_df['end_date'] == '20231231']['revenue'].values[0]\n",
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"\n",
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"# 获取期初和期末的应收账款\n",
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"balancesheet_df = ts_pro.balancesheet(ts_code='600519.SH', start_date='20220101', end_date='20241231',\n",
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" fields='end_date, accounts_receiv')\n",
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"accounts_receiv_initial = balancesheet_df[balancesheet_df['end_date'] == '20221231']['accounts_receiv'].values[0]\n",
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"accounts_receiv_final = balancesheet_df[balancesheet_df['end_date'] == '20231231']['accounts_receiv'].values[0]\n",
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"\n",
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"# 计算平均应收账款余额\n",
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"avg_accounts_receiv = (accounts_receiv_initial + accounts_receiv_final) / 2\n",
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"\n",
|
||
"# 计算应收账款周转率\n",
|
||
"accounts_receiv_turnover = revenue / avg_accounts_receiv\n",
|
||
"\n",
|
||
"# 计算收现日数\n",
|
||
"collection_period = 365 / accounts_receiv_turnover\n",
|
||
"\n",
|
||
"print(f'收现日数: {collection_period:.2f} 天')"
|
||
],
|
||
"id": "dca87ef21cf425ee",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"收现日数: 0.10 天\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 50
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-01-10T07:10:09.564888Z",
|
||
"start_time": "2025-01-10T07:10:08.771019Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"code = \"000672.SZ\"\n",
|
||
"year = \"2023\"\n",
|
||
"period = f\"{year}1231\"\n",
|
||
"last_period = f\"{int(year) - 1}1231\"\n",
|
||
"\n",
|
||
"balancesheet_fields = \"total_assets,total_hldr_eqy_inc_min_int,money_cap,accounts_receiv,inventories\"\n",
|
||
"balancesheet_df = ts_pro.balancesheet(ts_code=code, period=period, fields=balancesheet_fields)\n",
|
||
"last_balancesheet_df = ts_pro.balancesheet(ts_code=code, period=last_period, fields=balancesheet_fields)\n",
|
||
"\n",
|
||
"income_fields = \"n_income,revenue,total_revenue,oper_cost,operate_profit,total_opcost\"\n",
|
||
"income_df = ts_pro.income(ts_code=code, period=period, fields=income_fields)\n",
|
||
"last_income_df = ts_pro.income(ts_code=code, period=last_period, fields=income_fields)\n",
|
||
"\n",
|
||
"cashflow_fields = \"cash_paid_for_trade_fin\"\n",
|
||
"cashflow_df = ts_pro.cashflow(ts_code=code, period=period, fields=cashflow_fields)\n",
|
||
"last_cashflow_df = ts_pro.cashflow(ts_code=code, period=last_period, fields=cashflow_fields)"
|
||
],
|
||
"id": "524a18f138a84f4a",
|
||
"outputs": [],
|
||
"execution_count": 96
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-01-10T07:10:35.283014Z",
|
||
"start_time": "2025-01-10T07:10:35.268876Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ROE & ROA\n",
|
||
"roe = income_df[\"n_income\"].values[0] / ((balancesheet_df[\"total_hldr_eqy_inc_min_int\"].values[0] +\n",
|
||
" last_balancesheet_df[\"total_hldr_eqy_inc_min_int\"].values[0]) / 2)\n",
|
||
"roa = income_df[\"n_income\"].values[0] / (\n",
|
||
" (balancesheet_df[\"total_assets\"].values[0] + last_balancesheet_df[\"total_assets\"].values[0]) / 2)\n",
|
||
"print(f\"ROE: {roe:.4f}\")\n",
|
||
"print(f\"ROA: {roa:.4f}\")\n",
|
||
"\n",
|
||
"# 净利润（转换为百万为单位）\n",
|
||
"net_profit = income_df[\"n_income\"].values[0] / 1000000\n",
|
||
"print(f\"净利润: {net_profit:.4f}百万\")\n",
|
||
"\n",
|
||
"# 总资产周转率\n",
|
||
"total_asset_turnover = income_df[\"total_revenue\"].values[0] / (\n",
|
||
" (balancesheet_df[\"total_assets\"].values[0] + last_balancesheet_df[\"total_assets\"].values[0]) / 2)\n",
|
||
"print(f\"总资产周转率: {total_asset_turnover:.4f}\")\n",
|
||
"\n",
|
||
"# 现金比率\n",
|
||
"cash_ratio = + balancesheet_df[\"money_cap\"].values[0] / balancesheet_df[\"total_assets\"].values[0]\n",
|
||
"print(f\"现金比率: {cash_ratio:.4f}\")\n",
|
||
"\n",
|
||
"# 收现日数\n",
|
||
"collection_period = 360 / (income_df[\"total_revenue\"].values[0] / balancesheet_df[\"accounts_receiv\"].values[0])\n",
|
||
"print(f\"收现日数: {collection_period:.2f} 天\")\n",
|
||
"\n",
|
||
"# 销货日数\n",
|
||
"sales_period = 360 / ((income_df[\"total_revenue\"].values[0]) / balancesheet_df[\"inventories\"].values[0])\n",
|
||
"print(f\"销货日数: {sales_period:.2f} 天\")\n",
|
||
"\n",
|
||
"# 毛利率\n",
|
||
"gross_profit_margin = (income_df[\"total_revenue\"].values[0] - income_df[\"oper_cost\"].values[0]) / income_df[\n",
|
||
" \"revenue\"].values[0]\n",
|
||
"print(f\"毛利率: {gross_profit_margin:.4f}\")\n",
|
||
"\n",
|
||
"# 经营利润率\n",
|
||
"operating_profit_margin = income_df[\"operate_profit\"].values[0] / income_df[\"total_revenue\"].values[0]\n",
|
||
"print(f\"经营利润率: {operating_profit_margin:.4f}\")\n",
|
||
"print(f\"经营安全边际率: {(operating_profit_margin / gross_profit_margin):.4f}\")"
|
||
],
|
||
"id": "8707ca42171475a8",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ROE: 0.0747\n",
|
||
"ROA: 0.0411\n",
|
||
"净利润: 701.3791百万\n",
|
||
"总资产周转率: 0.3745\n",
|
||
"现金比率: 0.2253\n",
|
||
"收现日数: 12.95 天\n",
|
||
"销货日数: 59.70 天\n",
|
||
"毛利率: 0.2775\n",
|
||
"经营利润率: 0.1474\n",
|
||
"经营安全边际率: 0.5312\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 98
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-01-11T03:51:41.253282Z",
|
||
"start_time": "2025-01-11T03:51:41.243498Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def get_blancesheet_df(start_year, end_year):\n",
|
||
" fields = [\"ts_code\", \"end_date\", \"total_assets\", \"total_hldr_eqy_inc_min_int\", \"money_cap\", \"accounts_receiv\",\n",
|
||
" \"inventories\"]\n",
|
||
" result = pd.DataFrame(columns=fields)\n",
|
||
" for year in range(start_year, end_year + 1):\n",
|
||
" period = f\"{year}1231\"\n",
|
||
" temp = ts_pro.balancesheet_vip(period=period, fields=\",\".join(fields))\n",
|
||
" result = pd.concat([result, temp], ignore_index=True)\n",
|
||
" return result\n",
|
||
"\n",
|
||
"def get_income_df(start_year, end_year):\n",
|
||
" fields = [\"ts_code\", \"end_date\", \"n_income\", \"revenue\", \"total_revenue\", \"oper_cost\", \"operate_profit\", \"total_opcost\"]\n",
|
||
" result = pd.DataFrame(columns=fields)\n",
|
||
" for year in range(start_year, end_year + 1):\n",
|
||
" period = f\"{year}1231\"\n",
|
||
" temp = ts_pro.income_vip(period=period, fields=\",\".join(fields))\n",
|
||
" result = pd.concat([result, temp], ignore_index=True)\n",
|
||
" return result\n",
|
||
"\n",
|
||
"def clean_df(df):\n",
|
||
" df = df.drop_duplicates(subset=[\"ts_code\", \"end_date\"])\n",
|
||
" df[\"end_date\"] = df[\"end_date\"].str[:4]\n",
|
||
" return df"
|
||
],
|
||
"id": "6d90bcb4c5151976",
|
||
"outputs": [],
|
||
"execution_count": 54
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-01-11T03:51:53.799229Z",
|
||
"start_time": "2025-01-11T03:51:42.756645Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"blancesheet_df = clean_df(get_blancesheet_df(2018, 2024))\n",
|
||
"income_df = clean_df(get_income_df(2018, 2024))"
|
||
],
|
||
"id": "9d7013cff65167ed",
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_39014/3037579022.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",
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_39014/3037579022.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",
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_39014/3037579022.py:17: 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",
|
||
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_39014/3037579022.py:17: 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": 55
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-01-11T04:06:53.483655Z",
|
||
"start_time": "2025-01-11T04:06:53.417743Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"stock_df = pd.merge(blancesheet_df, income_df, on=[\"ts_code\", \"end_date\"])\n",
|
||
"test_df = stock_df[stock_df[\"ts_code\"] == \"000672.SZ\"].copy()\n",
|
||
"test_df[\"roe\"] = test_df[\"n_income\"] / ((test_df[\"total_hldr_eqy_inc_min_int\"] +\n",
|
||
" test_df[\"total_hldr_eqy_inc_min_int\"].shift(1)) / 2)\n",
|
||
"test_df[[\"ts_code\",\"end_date\",\"roe\"]]\n",
|
||
"# stock_group = income_df.groupby('ts_code')\n",
|
||
"# stock_group.get_group(\"000672.SZ\")"
|
||
],
|
||
"id": "f9317a2d18449db2",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" ts_code end_date roe\n",
|
||
"3346 000672.SZ 2018 NaN\n",
|
||
"8529 000672.SZ 2019 0.508885\n",
|
||
"12623 000672.SZ 2020 0.316230\n",
|
||
"17853 000672.SZ 2021 0.278297\n",
|
||
"22036 000672.SZ 2022 0.105235\n",
|
||
"28907 000672.SZ 2023 0.074670"
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" 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>ts_code</th>\n",
|
||
" <th>end_date</th>\n",
|
||
" <th>roe</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>3346</th>\n",
|
||
" <td>000672.SZ</td>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8529</th>\n",
|
||
" <td>000672.SZ</td>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>0.508885</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12623</th>\n",
|
||
" <td>000672.SZ</td>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>0.316230</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17853</th>\n",
|
||
" <td>000672.SZ</td>\n",
|
||
" <td>2021</td>\n",
|
||
" <td>0.278297</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22036</th>\n",
|
||
" <td>000672.SZ</td>\n",
|
||
" <td>2022</td>\n",
|
||
" <td>0.105235</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>28907</th>\n",
|
||
" <td>000672.SZ</td>\n",
|
||
" <td>2023</td>\n",
|
||
" <td>0.074670</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 68,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 68
|
||
}
|
||
],
|
||
"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
|
||
}
|