File size: 2,368 Bytes
a1eb1f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import gradio as gr
import pandas as pd
from tempfile import NamedTemporaryFile
def main(file: str):
df = pd.read_excel(file)
df.columns = df.columns.str.strip()
# Search the max '涨幅' with corresponding '名称' and '代码' for each '细分行业'
df_max = (
df.groupby("细分行业")
.apply(lambda x: x.loc[x["涨幅"].idxmax()], include_groups=False)
.reset_index()
)
df_max = df_max[["细分行业", "名称", "总市值"]]
# rename 名称 to 领涨股票, 总市值 to 领涨股票市值
df_max.columns = ["细分行业", "领涨股票", "领涨股票市值"]
df_max["领涨股票市值"] = df_max["领涨股票市值"] / 1e8
# Search the max '涨幅' with corresponding '名称' and '代码' for each '细分行业'
df_min = (
df.groupby("细分行业")
.apply(lambda x: x.loc[x["涨幅"].idxmin()], include_groups=False)
.reset_index()
)
df_min = df_min[["细分行业", "名称", "总市值"]]
# rename 名称 to 领涨股票, 总市值 to 领涨股票市值
df_min.columns = ["细分行业", "领跌股票", "领跌股票市值"]
df_min["领跌股票市值"] = df_min["领跌股票市值"] / 1e8
df_count = df.groupby("细分行业").count().reset_index()[["细分行业", "代码"]]
df_count.columns = ["细分行业", "股票数量"]
df_total_value = (
df[["细分行业", "总市值", "流通市值"]].groupby("细分行业").sum().reset_index()
)
df_total_value[["总市值", "流通市值"]] = (
df_total_value[["总市值", "流通市值"]] / 1e8
)
df_result = pd.concat(
[
df_count,
df_total_value[["总市值", "流通市值"]],
df_max[["领涨股票", "领涨股票市值"]],
df_min[["领跌股票", "领跌股票市值"]],
],
axis=1,
)
temp_file = NamedTemporaryFile(delete=False, suffix=".xlsx")
df_result.to_excel(temp_file, index=False)
return temp_file.name
with gr.Blocks() as demo:
file_input = gr.File(
file_count="single",
file_types=[".xlsx", ".xls"],
type="filepath",
label="输入文件",
)
file_output = gr.File()
button = gr.Button("开始处理")
button.click(main, inputs=[file_input], outputs=[file_output])
demo.launch()
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