Dask DataFrames are designed to be a drop-in replacement for Pandas DataFrames, with a nearly identical API. This means that you can use most of the same functions and methods that you would use with a Pandas DataFrame, but with the added benefit of being able to scale your computations to large datasets that do not fit in memory.

Here are some examples of how to use Dask DataFrame operations that are compatible with the Pandas API:

import dask.dataframe as dd
 
# Load a CSV file
df = dd.read_csv('path/to/mydata.csv')
 
# Select a subset of columns
subset = df[['column1', 'column2']]
 
# Filter rows based on a condition
filtered = df[df['column3'] > 0]
 
# Group by a column and count the number of rows in each group
grouped = df.groupby('column4').size()
 
# Join with another DataFrame
other = dd.read_csv('path/to/otherdata.csv')
joined = df.merge(other, on='key')
 
# Compute the mean of a column
mean = df['column5'].mean()
 
# Apply a function element-wise to a column
transformed = df['column6'].apply(lambda x: x ** 2)
 
# Resample time series data
timeseries = dd.read_csv('path/to/timeseries.csv', parse_dates=['date'])
resampled = timeseries.resample('D', on='date').sum()

In general, if you are familiar with the Pandas API, you should be able to use the same operations with Dask DataFrames. However, there are some differences and limitations to be aware of when using Dask, such as:

  • Some operations may not be supported by Dask, or may have different performance characteristics than with Pandas. For example, operations that require sorting may be slower with Dask than with Pandas.

  • Some operations may require explicit partitioning of the data. For example, the groupby() operation requires that the data be partitioned by the grouping column. If the data is not already partitioned, you may need to use the set_index() or repartition() operations to partition the data before using groupby().

  • Some operations may require additional memory overhead, because Dask needs to keep track of the computation graph and manage communication between workers. If you are working with very large datasets, you may need to adjust the memory_limit and processes parameters when creating a Dask client to optimize performance.

Overall, Dask provides a powerful way to scale your Pandas workflows to larger datasets, but it is important to be aware of the differences and limitations of the Dask API compared to Pandas.