# 使用ix进行下表和名称组合做引 data.ix[0:4, ['open', 'close', 'high', 'low']] # 推荐使用loc和iloc来获取的方式 data.loc[data.index[0:4], ['open', 'close', 'high', 'low']] data.iloc[0:4, data.columns.get_indexer(['open', 'close', 'high', 'low'])] open close hig...
可以使用df.columns命令对数据字段进行预览 df.columns 使用df.dtypes命令查看数据类型,其中,日期是日期型,区域为字符型,销售数为数值型。 df.dtypes 使用df.info()命令查看查看索引、数据类型和内存信息。 df.info() 对数据做基本的描述统计可以有以下特征: 数据包含7409行数据,客户平均年龄为42岁,最小年龄22岁,...
s.replace([1,3],['one','three']) # 'one'代替1,'three'代替3 df.rename(columns=lambdax:x+1) # 批量更改列名 df.rename(columns={'old_name':'new_ name'}) # 选择性更改列名 df.set_index('column_one') # 将某个字段设为索引,可接受列表参数,即设置多个索引 df.reset_index("col1") ...
For example, you have the columns “name”, “age”, “address”, and “marks” in a DataFrame. Any of the above columns may not have unique values for all the different rows and are unfit as indexes. However, the columns “name” and “address” together may uniquely identify each row...
thecolumns as the index, otherwise default integer index will be used.Parameters---sql : str SQL query or SQLAlchemy Selectable (select or text object)SQL query to be executed.con : SQLAlchemy connectable, str, or sqlite3 connectionUsing SQLAlchemy makes it possible to use any DB supported ...
random.randn(len(data), columns), columns=col_names)], axis=1) # IMPORTANT!!! This function is required for building any customized CLI loader. def find_loader(kwargs): test_data_opts = get_loader_options(LOADER_KEY, LOADER_PROPS, kwargs) if len([f for f in test_data_opts.values...
def using_query(df): return df.query('A > 50') Now, let’s time these functions using timeit: import timeit n_repeat = 3 n_iter = 10 where_time = timeit.timeit('using_where(df)', globals=globals(), number=n_iter) / n_iter ...
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more - pandas/pandas/io/html.py at v0.22.0 · pandas-dev/pandas
Because pandas understands date columns, you can express the date value in multiple formats and it will give you the results you expect. df[df['date']>='Oct-2014'].head() df[df['date']>='10-10-2014'].head() When working with time series data, if we convert the data to use the...
Finally, Arrow uses columnar data storage, which means that, regardless of the data type, all columns are stored in a continuous block of memory. This not only makes parallelism easier, but also makes data retrieval faster. Query optimizationCopy heading link ...