Table of Contents
Contents

pandas vs FiftyOne¶

This cheat sheet shows how to translate common pandas operations into FiftyOne.

Nomenclature¶

pandas

FiftyOne

DataFrame (df)

Dataset (ds)

Row

Sample

Column

Field

Getting started¶

pandas

FiftyOne

Importing the packages

import pandas as pd

import fiftyone as fo

Create empty dataset

df = pd.DataFrame()

ds = fo.Dataset()

Load dataset

df = pd.read_csv(*)

ds = fo.Dataset.from_dir(*)

Basics¶

pandas

FiftyOne

First row/sample

df.iloc[0] or df.head(1)

ds.first() or ds.head(1)

Last row/sample

df.iloc[-1] or df.tail(1)

ds.last() or ds.tail(1)

First few rows/samples

df.head()

ds.head()

Last few rows/samples

df.tail()

ds.tail()

Get specific row/sample

df.loc[j]

ds[sample_id]

Number of rows/samples

len(df)

len(ds)

Column names/field schema

df.columns

ds.get_field_schema()

Get all values in column/field

df[*].tolist()

ds.values(*)

View stages¶

pandas

FiftyOne

Make a copy

df.copy()

ds.clone()

Slice

df[start:end]

ds[start:end]

Random sample

df.sample(n=n)

ds.take(n)

Shuffle data

df.sample(frac=1)

ds.shuffle()

Filter by column/field value

df[df[*] > threshold]

ds.match(F(*) > threshold)

Sort values

df.sort_values()

ds.sort_by(*)

Delete all
import gc
del df; gc.collect()

ds.delete()

Aggregations¶

pandas

FiftyOne

Count

df[*].count()

ds.count(*)

Sum

df[*].sum()

ds.sum(*)

Unique values

df[*].unique()

ds.distinct(*)

Bounds
min = df[*].min()
max = df[*].max()

min, max = ds.bounds(*)

Mean

df[*].mean()

ds.mean(*)

Standard deviation

df[*].std()

ds.std(*)

Quantile

df[*].quantile(values)

ds.quantiles(*, values))

Structural changes¶

pandas

FiftyOne

New column/field as constant value

df["col"] = value
ds.add_sample_field("field", fo.StringField)
ds.set_field("field", value).save()

New column/field from external data

df["col"] = data

ds.set_values("field", data)

New column/field from existing columns/fields

df["col"] = df.apply(fcn, axis=1)
ds.add_sample_field("field", fo.FloatField)
ds.set_field("field", expression).save()

Remove a column/field

df = df.drop(["col"], axis=1)
ds.delete_sample_fields(["field"]) or
ds.exclude_fields(["field"]).keep_fields()

Keep only specified columns/fields

df["col1", "col2"]

ds.select_fields(["field1", "field2"])

Concatenate DataFrames or DatasetViews

pd.concat([df1, df2])

view1.concat(view2)

Add a single row/sample

df.append(row, ignore_index=True)

ds.add_sample(sample)

Remove rows/samples

df.drop(rows)
ds.delete_samples(sample_ids) or
ds.exclude(samples).keep()

Keep only specified rows/samples

df.iloc[rows]

ds.select(sample_ids)

Rename column/field

df.rename(columns={"old": "new"})

ds.rename_sample_field("old", "new")

Expressions¶

pandas

FiftyOne

Exact equality

df[df[*] == value]

ds.match(F(*) == value)

Less than or equal to

new_df = df[df[*] <= value]

new_view = ds.match(F(*) <= value)

Logical complement

new_df = df[~(df[*] <= value)]

new_view = ds.match(~(F(*) <= value))

Logical AND

df[pd_cond1 & pd_cond2]

ds.match(fo_cond1 & fo_cond2)

Logical OR

df[pd_cond1 | pd_cond2]

ds.match(fo_cond1 | fo_cond2)

Is in

df[*].isin(cols)

ds.filter_labels(*, F("label").is_in(fields))

Contains string

df[*].str.contains(substr)

ds.filter_labels(*, F("label").contains_str(substr))

Check for numerics

pdt.is_numeric_dtype(df[*])
isinstance(ds.get_field_schema()[*], (fo.FloatField, fo.IntField)) or
len(ds.match(F(*).is_number())) > 0

Check for strings

pdt.is_string_dtype(df[*])
isinstance(ds.get_field_schema()[*], fo.StringField) or
len(ds.match(F(*).is_string())) > 0

Check for null entries

df.isna().any()

len(ds.match(F(*) == None)) > 0

Note

The table above assumes you have imported:

import pandas.api.types as pdt
from fiftyone import ViewField as F