To read multiple CSV files using Dask DataFrames, you can use the dask.dataframe.read_csv() function with a list of file names or a glob pattern. This function will concatenate all the files into a single Dask DataFrame.
Here's an example of how to read multiple CSV files using Dask DataFrames:
import dask.dataframe as dddf = dd.read_csv('path/to/mydata*.csv') |
This will create a Dask DataFrame object called df that represents the data in all the CSV files that match the glob pattern "path/to/mydata*.csv". The * wildcard character matches any sequence of characters in the file name.
You can also pass a list of file names to the read_csv() function:
files = ['path/to/mydata1.csv', 'path/to/mydata2.csv', 'path/to/mydata3.csv']df = dd.read_csv(files) |
This will create a Dask DataFrame object called df that represents the data in all the CSV files in the list.
By default, Dask assumes that all the CSV files have a header row with column names and uses the first row as the header. If your CSV files do not have a header row, you can specify the header=None option in the read_csv() function.
Once you have created a Dask DataFrame, you can use various Dask DataFrame operations to process your data in parallel. For example, you can use the groupby operation to group the data by a column and then count the number of rows in each group:
grouped = df.groupby('column')count = grouped.size() |
To trigger the computation of your Dask DataFrame operations in parallel, you can use the dask.compute() function:
result = count.compute() |
This will parallelize the count operation across multiple cores or nodes, depending on your Dask setup.