在上述代码中,我们通过在read_csv方法中设置chunksize参数来读取数据块。在遍历过程中,我们会得到一个包含数据块的DataFrame,我们可以通过shape属性来查看每个块的大小。需要注意的是,enumerate方法可以用来获取数据块的索引值。 接下来,我们需要注意的是,由于数据块的大小并不相同,因此在使用块内数据时需要进行一些...
DataFrame.astype() 方法可对整个DataFrame或某一列进行数据格式转换,支持Python和NumPy的数据类型。 df['Name'] = df['Name'].astype(np.datetime64) 对数据聚合,我测试了 DataFrame.groupby 和DataFrame.pivot_table 以及 pandas.merge ,groupby 9800万行 x 3列的时间为99秒,连接表为26秒,生成透视表的速度更...
#三种方法 # Solution 1: Use chunks and for-loop df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', chunksize=50) df2 = pd.DataFrame() for chunk in df: df2 = df2.append(chunk.iloc[0,:]) # Solution 2: Use chunks and list comprehension ...
The read_csv() function offers a handy chunksize parameter, allowing you to read the data in smaller, manageable chunks. By setting the chunksize parameter, read_csv() returns an iterable object where each iteration provides a chunk of data as a pandas dataframe. This approach is particularly ...
You may come across scenarios where you need to bin continuous data into discrete chunks to be used as a categorical variable. We can use pd.cut() function to cut our data into discrete buckets. # Bin data into 5 equal sized buckets pd.cut(tips_data['total_bill'], bins=5) 0 (12.61...
Modinis a DataFrame for datasets from 1MB to 1TB+. It comes into play when you want to supercharge your DataFrame operations. It's like putting a turbocharger on Pandas to speed up data manipulation tasks by distributing them across all your CPU cores. Perhaps, the best part is it's com...
Now say you have aDataFramewith adatecolumn and want to offset it by a given number of days. Below, you’ll find two ways of doing that. Can you guess the speedup factor of the vectorized operation? By using vectorized operations rather than loops for this costly operation, we got an ...
pd.DataFrame(dict):从字典对象导入数据,Key是列名,Value是数据 df.to_csv(filename):导出数据到CSV文件 df.to_excel(filename):导出数据到Excel文件 df.to_sql(table_name, connection_object):导出数据到SQL表 df.to_json(filename):以Json格式导出数据到文本文件 ...
DataFrame.unstack(level=-1,fill_value=None)level：默认为-1，表示操作内层索引，0表示操作外层索引。