# pandas vs FiftyOne
Available in:
Open SourceEnterprise
This cheat sheet shows how to translate common
[pandas](https://pandas.pydata.org) 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:
```python
import pandas.api.types as pdt
from fiftyone import ViewField as F
```