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| |
| # Basic Operations |
| |
| In this section, you will learn how to display essential details of DataFrames using specific functions. |
| |
| ```{code-cell} ipython3 |
| from datafusion import SessionContext |
| import random |
| |
| ctx = SessionContext() |
| df = ctx.from_pydict({ |
| "nrs": [1, 2, 3, 4, 5], |
| "names": ["python", "ruby", "java", "haskell", "go"], |
| "random": random.sample(range(1000), 5), |
| "groups": ["A", "A", "B", "C", "B"], |
| }) |
| df |
| ``` |
| |
| Use {py:func}`~datafusion.dataframe.DataFrame.limit` to view the top rows of the frame: |
| |
| ```{code-cell} ipython3 |
| df.limit(2) |
| ``` |
| |
| Display the columns of the DataFrame using {py:func}`~datafusion.dataframe.DataFrame.schema`: |
| |
| ```{code-cell} ipython3 |
| df.schema() |
| ``` |
| |
| The method {py:func}`~datafusion.dataframe.DataFrame.to_pandas` uses pyarrow to convert to pandas DataFrame, by collecting the batches, |
| passing them to an Arrow table, and then converting them to a pandas DataFrame. |
| |
| ```{code-cell} ipython3 |
| df.to_pandas() |
| ``` |
| |
| {py:func}`~datafusion.dataframe.DataFrame.describe` shows a quick statistic summary of your data: |
| |
| ```{code-cell} ipython3 |
| df.describe() |
| ``` |