# 处理缺失值df = pd.read_excel(file_path, na_values=['nan','missing'])print(df) 2. 写入 Excel 文件 使用DataFrame.to_excel()方法可以将DataFrame对象的数据保存为 Excel 文件,同样支持多个参数来控制文件的输出格式。 基本写入 # 创建一个示例 DataFramedata = {'Name': ['Alice','Bob','Charlie...
# 使用dtype指定列的数据类型 df_dtype = pd.read_excel("example.xlsx", dtype={'ID': int, 'Name': str}, engine='openpyxl') print(df_dtype) # 使用chunksize分块读取大文件 with pd.read_excel("example.xlsx", chunksize=1000, engine='openpyxl') as reader: for chunk in reader: print(chun...
import pandas as pd # 假定你的Excel文件名为 example.xlsx,它在当前目录下 file_path = 'example.xlsx' # 使用 read_excel 函数读取文件 df = pd.read_excel(file_path) # 打印DataFrame查看数据 print(df) 这段代码将读取Excel文件example.xlsx中的第一个工作表,并将数据加载到DataFrame对...
xl = pd.ExcelFile(file_path)# In this case, there was only a single Worksheet in the Workbook.sheetname = xl.sheet_names[0]# Read the header outside of the loop, so all chunk reads are# consistent across all loop iterations.df_header = pd.read_excel(file_path, sheetname=sheetname,...
pd.read_excel(xls, sheet_name, chunksize=chunk_size): process_data(chunk) # 处理当前数据...
3、分块读取大文件如果要处理超大文件,可以使用`chunksize`参数分块读取。示例代码:分块读取大文件```pythonchunk_iter = pd.read_csv("big_data.csv", chunksize=10000)for chunk in chunk_iter: process(chunk)```实战案例 实践案例:电商销售数据分析 案例背景 假设我们有一个电商平台的销售数据集,包含...
for chunk in pd.read_csv('large_file.csv', chunksize=chunksize): # 对每个块进行处理,例如筛选、排序等 process(chunk) 2.2 读取Excel文件 import pandas as pd 读取Excel文件,每个块包含1000行数据 chunksize = 1000 for chunk in pd.read_excel('large_file.xlsx', sheet_name='Sheet1', chunksize=ch...
read_excel(file_path, sheetname=sheetname, nrows=1) # print(f"Excel file: {file_name} (worksheet: {sheetname})") print(f"文件名:{file_name}") print(f"工作表:{sheetname}") chunks = [] i_chunk = 0 # The first row is the header. We have already read it, so we skip it. ...
当通过read_csv、read_excel或其他数据帧读取函数将数据帧加载到内存中时,pandas会进行类型推断,这可能是低效的。这些api允许您明确地利用dtypes指定每个列的类型。指定dtypes允许在内存中更有效地存储数据。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ...
df_chunk = pd.read_excel( file_path, sheetname=sheetname, nrows=nrows, skiprows=skiprows, header=None) skiprows += nrows# When there is no data, we know we can break out of the loop.ifnotdf_chunk.shape[0]:breakelse:print(f" - chunk{i_chunk}({df_chunk.shape[0]}rows)") ...