return data.drop_duplicates()

This code would remove all duplicated rows from the DataFrame 'data' and return the modified DataFrame. The 'drop_duplicates()' method checks for duplicated rows based on all columns by default, but you can specify specific columns by passing them as arguments to the method.

相关问题

优化代码 def cluster_format(self, start_time, end_time, save_on=True, data_clean=False, data_name=None): """ local format function is to format data from beihang. :param start_time: :param end_time: :return: """ # 户用簇级数据清洗 if data_clean: unused_index_col = [i for i in self.df.columns if 'Unnamed' in i] self.df.drop(columns=unused_index_col, inplace=True) self.df.drop_duplicates(inplace=True, ignore_index=True) self.df.reset_index(drop=True, inplace=True) dupli_header_lines = np.where(self.df['sendtime'] == 'sendtime')[0] self.df.drop(index=dupli_header_lines, inplace=True) self.df = self.df.apply(pd.to_numeric, errors='ignore') self.df['sendtime'] = pd.to_datetime(self.df['sendtime']) self.df.sort_values(by='sendtime', inplace=True, ignore_index=True) self.df.to_csv(data_name, index=False) # 调用基本格式化处理 self.df = super().format(start_time, end_time) module_number_register = np.unique(self.df['bat_module_num']) # if registered m_num is 0 and not changed, there is no module data if not np.any(module_number_register): logger.logger.warning("No module data!") sys.exit() if 'bat_module_voltage_00' in self.df.columns: volt_ref = 'bat_module_voltage_00' elif 'bat_module_voltage_01' in self.df.columns: volt_ref = 'bat_module_voltage_01' elif 'bat_module_voltage_02' in self.df.columns: volt_ref = 'bat_module_voltage_02' else: logger.logger.warning("No module data!") sys.exit() self.df.dropna(axis=0, subset=[volt_ref], inplace=True) self.df.reset_index(drop=True, inplace=True) self.headers = list(self.df.columns) # time duration of a cluster self.length = len(self.df) if self.length == 0: logger.logger.warning("After cluster data clean, no effective data!") raise ValueError("No effective data after cluster data clean.") self.cluster_stats(save_on) for m in range(self.mod_num): print(self.clusterid, self.mod_num) self.module_list.append(np.unique(self.df[f'bat_module_sn_{str(m).zfill(2)}'].dropna())[0])

Here are some possible optimizations for the given code:

1. Instead of using a list comprehension to find columns with 'Unnamed' in their names, you can use the `filter()` function along with a lambda function to achieve the same result in a more concise way:

   unused_index_col = list(filter(lambda x: 'Unnamed' in x, self.df.columns))

2. Instead of dropping duplicates and resetting the index separately, you can use the `drop_duplicates()` function with the `ignore_index` parameter set to `True` to achieve both in one step:

   self.df.drop_duplicates(inplace=True, ignore_index=True)

3. Instead of using `sys.exit()` to terminate the program when there is no module data, you can raise a `ValueError` with an appropriate error message:

   raise ValueError("No module data!")

4. Instead of using a series of `if` statements to find the voltage reference column, you can use the `loc` accessor with a boolean mask to select the first column that starts with 'bat_module_voltage':

   volt_ref_col = self.df.columns[self.df.columns.str.startswith('bat_module_voltage')][0]

5. Instead of using a loop to append a single item to a list, you can use the `append()` method directly:

   self.module_list.append(np.unique(self.df[f'bat_module_sn_{str(m).zfill(2)}'].dropna())[0])

By applying these optimizations, the code can become more concise and efficient.

import pandas as pd import numpy as np #消除警告的命令 import warnings warnings.filterwarnings("ignore") #打开表 def open_data_def(): data_df = pd.read_csv("transactions.csv",encoding="gbk") #将列表题修改为中文的 data_df.columns = ["客户号","银行卡号","刷卡金额","刷卡时间","卡类型"] if data_df.duplicated().sum(): use_df = data_df.drop_duplicates() return use_df #添加时间信息函数 def add_time(): use_df = open_data_def() #打开表函数 use_df["刷卡时间"]=pd.to_datetime(use_df.loc[:,"刷卡时间"]) print(use_df.dtypes) use_df["年"]=use_df.loc[:,"刷卡时间"].dt.year use_df["月"]=use_df.loc[:,"刷卡时间"].dt.month use_df["日"]=use_df.loc[:,"刷卡时间"].dt.day use_df["时"]=use_df.loc[:,"刷卡时间"].dt.hour return use_df use_df = add_time() #调用添加时间信息函数 use_df整合按客户号统计的代码与按时间统计的代码到函数中：效果为：可以通过输入"客户号"或"时间"来选择相应 的函数进行操作

好的，我会将你提供的代码整合到一个函数中，并添加选择功能。以下是整合后的代码：

import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")

def open_data_def():
    data_df = pd.read_csv("transactions.csv", encoding="gbk")
    data_df.columns = ["客户号", "银行卡号", "刷卡金额", "刷卡时间", "卡类型"]
    if data_df.duplicated().sum():
        use_df = data_df.drop_duplicates()
    return use_df

def add_time():
    use_df = open_data_def()
    use_df["刷卡时间"] = pd.to_datetime(use_df.loc[:, "刷卡时间"])
    use_df["年"] = use_df.loc[:, "刷卡时间"].dt.year
    use_df["月"] = use_df.loc[:, "刷卡时间"].dt.month
    use_df["日"] = use_df.loc[:, "刷卡时间"].dt.day
    use_df["时"] = use_df.loc[:, "刷卡时间"].dt.hour
    return use_df

def customer_or_time_statistics(option):
    use_df = add_time()
    
    if option == "客户号":
        # 按客户号统计的代码
        customer_statistics_result = use_df.groupby("客户号").agg({"刷卡金额": np.sum})
        return customer_statistics_result
    elif option == "时间":
        # 按时间统计的代码
        time_statistics_result = use_df.groupby(["年", "月"]).agg({"刷卡金额": np.sum})
        return time_statistics_result
    else:
        return "无效选项，请输入'客户号'或'时间'。"

# 通过输入选择相应的函数进行操作
option = input("请输入选项（客户号/时间）：")
result = customer_or_time_statistics(option)
print(result)

在上面的代码中，`customer_or_time_statistics` 函数接受一个选项参数 `option`，根据用户输入的选项，函数将执行相应的统计代码并返回结果。请注意，代码中使用了 `input` 函数来获取用户的选项输入。你可以根据实际情况进行调整和修改。

这样，你就可以通过输入"客户号"或"时间"来选择相应的函数进行操作了。