import datatable as dt

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# load a CSV file

df = dt.fread("mydata.csv")

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# print the first 10 rows

print(df.head(10))

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# select a subset of columns

df_sub = df[:, ["col1", "col2"]]

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# filter rows based on a condition

df_filtered = df[df["col1"] > 10]

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# group by a column and compute the mean of another column

df_grouped = df[:, {"mean_col2": dt.mean(dt.f.col2)}, dt.by("col1")]

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# join two tables on a common column

df2 = dt.fread("mydata2.csv")

df_joined = df[:, :, dt.join(df2, "common_col")]

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# write the result to a new CSV file

df_joined.to_csv("result.csv")

​