To read a CSV file using Dask DataFrames, you can use the dask.dataframe.read_csv() function. This function is similar to the Pandas read_csv() function, but it can handle large datasets that don't fit into memory.
Here's an example of how to read a CSV file using Dask DataFrames:
import dask.dataframe as dddf = dd.read_csv('mydata.csv') |
This will create a Dask DataFrame object called df that represents the data in the CSV file named "mydata.csv". By default, Dask assumes that the CSV file has a header row with column names and uses the first row as the header. If your CSV file does not have a header row, you can specify the header=None option in the read_csv() function:
df = dd.read_csv('mydata.csv', header=None) |
You can also specify the delimiter character used in the CSV file using the delimiter or sep parameter. By default, Dask assumes that the delimiter is a comma. Here's an example of reading a tab-separated CSV file:
df = dd.read_csv('mydata.tsv', delimiter='\t') |
If your CSV file has missing values or other special characters, you can use various options such as na_values or encoding to handle them. Here's an example of reading a CSV file with missing values:
df = dd.read_csv('mydata.csv', na_values=['NA', 'NULL']) |
This will replace any occurrence of "NA" or "NULL" with a missing value in the resulting Dask DataFrame.
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.