简易实现

 
df_res.loc[:,"result_sim"] = df_res["result_sim"].apply(lambda x: float(f"{x:.4g}"))*100
    

to_numeric

 
import pandas as pd

# 示例 DataFrame
df = pd.DataFrame({'col_str': ['1', '2', '3', '4.5']})

# 转换为数字
df['col_num'] = pd.to_numeric(df['col_str'])

print(df.dtypes)  # 查看类型
    

处理无法转换的值

 
# 方法1：将无法转换的值设为 NaN
df['col_num'] = pd.to_numeric(df['col_str'], errors='coerce')

# 方法2：忽略无法转换的值（保持原样）
df['col_num'] = pd.to_numeric(df['col_str'], errors='ignore')

# 方法3：遇到无法转换的值报错
df['col_num'] = pd.to_numeric(df['col_str'], errors='raise')  # 默认行为

    

astype()

 
# 转换为浮点数
df['col_float'] = df['col_str'].astype(float)

# 转换为整数
df['col_int'] = df['col_str'].astype(int)  # 注意：不能有小数部分

特殊字符

 
# 移除美元符号
df['col_clean'] = df['col_str'].str.replace('$', '')
df['col_num'] = pd.to_numeric(df['col_clean'])

# 移除百分号并转换为小数
df['col_clean'] = df['col_str'].str.replace('%', '')
df['col_num'] = pd.to_numeric(df['col_clean']) / 100

转换整个 DataFrame

 
# 转换整个 DataFrame 中的所有可转换的列
df = df.apply(pd.to_numeric, errors='ignore')