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Choose a pandas selector by asking two questions: are you identifying rows and columns by labels or by integer positions, and do you need one value or a larger selection? Use [] for a single column or a simple row filter, .loc for label-based selections, .iloc for zero-based positional selections, and .at or .iat for a single scalar.
How do I select a subset of a DataFrame?
Start with this small DataFrame. Its index labels are strings, so the distinction between a label and a row position is visible:
import pandas as pd
df = pd.DataFrame(
{
"name": ["Ada", "Linus", "Grace"],
"age": [36, 34, 40],
"city": ["London", "Helsinki", "New York"],
},
index=["row_a", "row_b", "row_c"],
)For example, row_a is the label of the first row, while 0 is its zero-based position. The core distinction is:
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Use [] for a column or a straightforward row filter
Select a column
Pass the column label in brackets to get a Series:
df["name"]This returns the name values for all rows. To select more than one named column, pass a list of labels; the result is a DataFrame:
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df[["name", "age"]]Filter rows with a condition
A boolean condition inside brackets keeps rows where the condition is true:
df[df["age"] > 35]The condition produces a Boolean Series aligned with the DataFrame’s rows. You can also use an explicit label-based selection when you want to state the row condition and chosen columns together:
df.loc[df["age"] > 35, "name"]This returns the names of people older than 35. The pandas getting-started tutorial demonstrates this pattern with its Titanic data, selecting names where Age is greater than 35 (pandas getting-started tutorial).
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Use .loc when selectors are labels
The general form is df.loc[row_selector, column_selector]. The selector before the comma addresses rows; the selector after it addresses columns. Use : when you want all rows or all columns on one axis.
# Rows by index labels, and columns by names
df.loc["row_a", "name"]
# All rows, selected columns
df.loc[:, ["name", "age"]]
# A range of row labels and two columns
df.loc["row_a":"row_c", ["name", "age"]]Unlike ordinary Python slicing, a .loc label slice includes its stop label when that label is present. Thus "row_c" is included in the last example. A number passed to .loc is still interpreted as an index label, not as a row number. If a requested label does not exist, .loc raises KeyError.
Use .iloc when selectors are integer positions
The form is df.iloc[row_position, column_position]. Positions start at zero: position 0 is the first row or column. For example:
# First row, second column
df.iloc[0, 1]
# First three rows, second and third columns
df.iloc[0:3, [1, 2]]In the second example, the row slice includes positions 0, 1, and 2, while the selected column positions are 1 and 2. Positional slices follow Python and NumPy rules: the start is included and the stop is excluded. A scalar or list request for a position outside the axis raises IndexError; slice endpoints may extend out of bounds without that error.
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Use .at and .iat to get or set one value
When the desired result is one scalar rather than a Series or DataFrame, use the scalar accessor that matches your selector:
| Accessor | Selector meaning | Example |
|---|---|---|
.at |
Row and column labels | df.at["row_a", "age"] |
.iat |
Zero-based row and column positions | df.iat[0, 1] |
Both examples retrieve Ada’s age from this DataFrame. These accessors can also assign a value, for example df.at["row_a", "age"] = 37. Assignment through .at to a missing indexer can enlarge the object in place; take care when an apparent typo could create a new entry. The pandas API reference documents .iat as the integer-position scalar accessor (DataFrame.iat API reference).
Which selector should you use?
| Need | Use | What to remember |
|---|---|---|
| One named column | df["column"] |
Returns a Series. |
| Rows matching a simple condition | df[condition] |
Boolean filtering keeps matching rows. |
| A selection by row or column labels | df.loc[rows, columns] |
Label slices include their stop label when present. |
| A selection by row or column positions | df.iloc[rows, columns] |
Positions start at zero; slice stops are excluded. |
| One value by labels | df.at[row_label, column_label] |
Scalar label lookup. |
| One value by positions | df.iat[row_position, column_position] |
Scalar positional lookup. |
The [] syntax is convenient for columns and uncomplicated filtering. For production code, the pandas user guide recommends the explicit access methods .at, .iat, .loc, and .iloc; it does not supply a benchmark or a numeric performance advantage for these methods (pandas indexing and selecting data guide). For version-specific details, consult the documentation matching the pandas version installed in your environment; the guide and API references may describe different releases as they are updated.
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