# Intro to data structures

We’ll start with a quick, non-comprehensive overview of the fundamental data structures in pandas to get you started. The fundamental behavior about data types, indexing, and axis labeling / alignment apply across all of the objects. To get started, import NumPy and load pandas into your namespace:

    In [1]: import numpy as np

    In [2]: import pandas as pd

Here is a basic tenet to keep in mind: data alignment is intrinsic. The link between labels and data will not be broken unless done so explicitly by you.

We’ll give a brief intro to the data structures, then consider all of the broad categories of functionality and methods in separate sections.

## Series

`Series` is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the index. The basic method to create a Series is to call:

    >>> s = pd.Series(data, index=index)

Here, `data` can be many different things:

- a Python dict

- an ndarray

- a scalar value (like 5)

The passed index is a list of axis labels. Thus, this separates into a few cases depending on what data is:

From ndarray

If `data` is an ndarray, index must be the same length as data. If no index is passed, one will be created having values `[0, ..., len(data) - 1]`.

    In [3]: s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])

    In [4]: s
    Out[4]:
    a    0.469112
    b   -0.282863
    c   -1.509059
    d   -1.135632
    e    1.212112
    dtype: float64

    In [5]: s.index
    Out[5]: Index(['a', 'b', 'c', 'd', 'e'], dtype='object')

    In [6]: pd.Series(np.random.randn(5))
    Out[6]:
    0   -0.173215
    1    0.119209
    2   -1.044236
    3   -0.861849
    4   -2.104569
    dtype: float64

Note

pandas supports non-unique index values. If an operation that does not support duplicate index values is attempted, an exception will be raised at that time. The reason for being lazy is nearly all performance-based (there are many instances in computations, like parts of GroupBy, where the index is not used).

From dict

Series can be instantiated from dicts:

    In [7]: d = {"b": 1, "a": 0, "c": 2}

    In [8]: pd.Series(d)
    Out[8]:
    b    1
    a    0
    c    2
    dtype: int64

Note

When the data is a dict, and an index is not passed, the `Series` index will be ordered by the dict’s insertion order, if you’re using Python version >= 3.6 and pandas version >= 0.23.

If you’re using Python < 3.6 or pandas < 0.23, and an index is not passed, the `Series` index will be the lexically ordered list of dict keys.

In the example above, if you were on a Python version lower than 3.6 or a pandas version lower than 0.23, the `Series` would be ordered by the lexical order of the dict keys (i.e. `['a', 'b', 'c']` rather than `['b', 'a', 'c']`).

If an index is passed, the values in data corresponding to the labels in the index will be pulled out.

    In [9]: d = {"a": 0.0, "b": 1.0, "c": 2.0}

    In [10]: pd.Series(d)
    Out[10]:
    a    0.0
    b    1.0
    c    2.0
    dtype: float64

    In [11]: pd.Series(d, index=["b", "c", "d", "a"])
    Out[11]:
    b    1.0
    c    2.0
    d    NaN
    a    0.0
    dtype: float64

Note

NaN (not a number) is the standard missing data marker used in pandas.

From scalar value

If `data` is a scalar value, an index must be provided. The value will be repeated to match the length of index.

    In [12]: pd.Series(5.0, index=["a", "b", "c", "d", "e"])
    Out[12]:
    a    5.0
    b    5.0
    c    5.0
    d    5.0
    e    5.0
    dtype: float64

### Series is ndarray-like

`Series` acts very similarly to a `ndarray`, and is a valid argument to most NumPy functions. However, operations such as slicing will also slice the index.

    In [13]: s[0]
    Out[13]: 0.4691122999071863

    In [14]: s[:3]
    Out[14]:
    a    0.469112
    b   -0.282863
    c   -1.509059
    dtype: float64

    In [15]: s[s > s.median()]
    Out[15]:
    a    0.469112
    e    1.212112
    dtype: float64

    In [16]: s[[4, 3, 1]]
    Out[16]:
    e    1.212112
    d   -1.135632
    b   -0.282863
    dtype: float64

    In [17]: np.exp(s)
    Out[17]:
    a    1.598575
    b    0.753623
    c    0.221118
    d    0.321219
    e    3.360575
    dtype: float64

Note

We will address array-based indexing like `s[[4, 3, 1]]` in section on indexing.

Like a NumPy array, a pandas Series has a `dtype`.

    In [18]: s.dtype
    Out[18]: dtype('float64')

This is often a NumPy dtype. However, pandas and 3rd-party libraries extend NumPy’s type system in a few places, in which case the dtype would be an `ExtensionDtype`. Some examples within pandas are Categorical data and Nullable integer data type. See dtypes for more.

If you need the actual array backing a `Series`, use `Series.array`.

    In [19]: s.array
    Out[19]:
    <PandasArray>
    [ 0.4691122999071863, -0.2828633443286633, -1.5090585031735124,
     -1.1356323710171934,  1.2121120250208506]
    Length: 5, dtype: float64

Accessing the array can be useful when you need to do some operation without the index (to disable automatic alignment, for example).

`Series.array` will always be an `ExtensionArray`. Briefly, an ExtensionArray is a thin wrapper around one or more concrete arrays like a `numpy.ndarray`. pandas knows how to take an `ExtensionArray` and store it in a `Series` or a column of a `DataFrame`. See dtypes for more.

While Series is ndarray-like, if you need an actual ndarray, then use `Series.to_numpy()`.

    In [20]: s.to_numpy()
    Out[20]: array([ 0.4691, -0.2829, -1.5091, -1.1356,  1.2121])

Even if the Series is backed by a `ExtensionArray`, `Series.to_numpy()` will return a NumPy ndarray.

### Series is dict-like

A Series is like a fixed-size dict in that you can get and set values by index label:

    In [21]: s["a"]
    Out[21]: 0.4691122999071863

    In [22]: s["e"] = 12.0

    In [23]: s
    Out[23]:
    a     0.469112
    b    -0.282863
    c    -1.509059
    d    -1.135632
    e    12.000000
    dtype: float64

    In [24]: "e" in s
    Out[24]: True

    In [25]: "f" in s
    Out[25]: False

If a label is not contained, an exception is raised:

    >>> s["f"]
    KeyError: 'f'

Using the `get` method, a missing label will return None or specified default:

    In [26]: s.get("f")

    In [27]: s.get("f", np.nan)
    Out[27]: nan

See also the section on attribute access.

### Vectorized operations and label alignment with Series

When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with Series in pandas. Series can also be passed into most NumPy methods expecting an ndarray.

    In [28]: s + s
    Out[28]:
    a     0.938225
    b    -0.565727
    c    -3.018117
    d    -2.271265
    e    24.000000
    dtype: float64

    In [29]: s * 2
    Out[29]:
    a     0.938225
    b    -0.565727
    c    -3.018117
    d    -2.271265
    e    24.000000
    dtype: float64

    In [30]: np.exp(s)
    Out[30]:
    a         1.598575
    b         0.753623
    c         0.221118
    d         0.321219
    e    162754.791419
    dtype: float64

A key difference between Series and ndarray is that operations between Series automatically align the data based on label. Thus, you can write computations without giving consideration to whether the Series involved have the same labels.

    In [31]: s[1:] + s[:-1]
    Out[31]:
    a         NaN
    b   -0.565727
    c   -3.018117
    d   -2.271265
    e         NaN
    dtype: float64

The result of an operation between unaligned Series will have the union of the indexes involved. If a label is not found in one Series or the other, the result will be marked as missing `NaN`. Being able to write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures set pandas apart from the majority of related tools for working with labeled data.

Note

In general, we chose to make the default result of operations between differently indexed objects yield the union of the indexes in order to avoid loss of information. Having an index label, though the data is missing, is typically important information as part of a computation. You of course have the option of dropping labels with missing data via the dropna function.

### Name attribute

Series can also have a `name` attribute:

    In [32]: s = pd.Series(np.random.randn(5), name="something")

    In [33]: s
    Out[33]:
    0   -0.494929
    1    1.071804
    2    0.721555
    3   -0.706771
    4   -1.039575
    Name: something, dtype: float64

    In [34]: s.name
    Out[34]: 'something'

The Series `name` will be assigned automatically in many cases, in particular when taking 1D slices of DataFrame as you will see below.

You can rename a Series with the `pandas.Series.rename()` method.

    In [35]: s2 = s.rename("different")

    In [36]: s2.name
    Out[36]: 'different'

Note that `s` and `s2` refer to different objects.

## DataFrame

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used pandas object. Like Series, DataFrame accepts many different kinds of input:

- Dict of 1D ndarrays, lists, dicts, or Series

- 2-D numpy.ndarray

- Structured or record ndarray

- A `Series`

- Another `DataFrame`

Along with the data, you can optionally pass index (row labels) and columns (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting DataFrame. Thus, a dict of Series plus a specific index will discard all data not matching up to the passed index.

If axis labels are not passed, they will be constructed from the input data based on common sense rules.

Note

When the data is a dict, and `columns` is not specified, the `DataFrame` columns will be ordered by the dict’s insertion order, if you are using Python version >= 3.6 and pandas >= 0.23.

If you are using Python < 3.6 or pandas < 0.23, and `columns` is not specified, the `DataFrame` columns will be the lexically ordered list of dict keys.

### From dict of Series or dicts

The resulting index will be the union of the indexes of the various Series. If there are any nested dicts, these will first be converted to Series. If no columns are passed, the columns will be the ordered list of dict keys.

    In [37]: d = {
       ....:     "one": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
       ....:     "two": pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "b", "c", "d"]),
       ....: }
       ....:

    In [38]: df = pd.DataFrame(d)

    In [39]: df
    Out[39]:
       one  two
    a  1.0  1.0
    b  2.0  2.0
    c  3.0  3.0
    d  NaN  4.0

    In [40]: pd.DataFrame(d, index=["d", "b", "a"])
    Out[40]:
       one  two
    d  NaN  4.0
    b  2.0  2.0
    a  1.0  1.0

    In [41]: pd.DataFrame(d, index=["d", "b", "a"], columns=["two", "three"])
    Out[41]:
       two three
    d  4.0   NaN
    b  2.0   NaN
    a  1.0   NaN

The row and column labels can be accessed respectively by accessing the index and columns attributes:

Note

When a particular set of columns is passed along with a dict of data, the passed columns override the keys in the dict.

    In [42]: df.index
    Out[42]: Index(['a', 'b', 'c', 'd'], dtype='object')

    In [43]: df.columns
    Out[43]: Index(['one', 'two'], dtype='object')

### From dict of ndarrays / lists

The ndarrays must all be the same length. If an index is passed, it must clearly also be the same length as the arrays. If no index is passed, the result will be `range(n)`, where `n` is the array length.

    In [44]: d = {"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]}

    In [45]: pd.DataFrame(d)
    Out[45]:
       one  two
    0  1.0  4.0
    1  2.0  3.0
    2  3.0  2.0
    3  4.0  1.0

    In [46]: pd.DataFrame(d, index=["a", "b", "c", "d"])
    Out[46]:
       one  two
    a  1.0  4.0
    b  2.0  3.0
    c  3.0  2.0
    d  4.0  1.0

### From structured or record array

This case is handled identically to a dict of arrays.

    In [47]: data = np.zeros((2,), dtype=[("A", "i4"), ("B", "f4"), ("C", "a10")])

    In [48]: data[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]

    In [49]: pd.DataFrame(data)
    Out[49]:
       A    B         C
    0  1  2.0  b'Hello'
    1  2  3.0  b'World'

    In [50]: pd.DataFrame(data, index=["first", "second"])
    Out[50]:
            A    B         C
    first   1  2.0  b'Hello'
    second  2  3.0  b'World'

    In [51]: pd.DataFrame(data, columns=["C", "A", "B"])
    Out[51]:
              C  A    B
    0  b'Hello'  1  2.0
    1  b'World'  2  3.0

Note

DataFrame is not intended to work exactly like a 2-dimensional NumPy ndarray.

### From a list of dicts

    In [52]: data2 = [{"a": 1, "b": 2}, {"a": 5, "b": 10, "c": 20}]

    In [53]: pd.DataFrame(data2)
    Out[53]:
       a   b     c
    0  1   2   NaN
    1  5  10  20.0

    In [54]: pd.DataFrame(data2, index=["first", "second"])
    Out[54]:
            a   b     c
    first   1   2   NaN
    second  5  10  20.0

    In [55]: pd.DataFrame(data2, columns=["a", "b"])
    Out[55]:
       a   b
    0  1   2
    1  5  10

### From a dict of tuples

You can automatically create a MultiIndexed frame by passing a tuples dictionary.

    In [56]: pd.DataFrame(
       ....:     {
       ....:         ("a", "b"): {("A", "B"): 1, ("A", "C"): 2},
       ....:         ("a", "a"): {("A", "C"): 3, ("A", "B"): 4},
       ....:         ("a", "c"): {("A", "B"): 5, ("A", "C"): 6},
       ....:         ("b", "a"): {("A", "C"): 7, ("A", "B"): 8},
       ....:         ("b", "b"): {("A", "D"): 9, ("A", "B"): 10},
       ....:     }
       ....: )
       ....:
    Out[56]:
           a              b
           b    a    c    a     b
    A B  1.0  4.0  5.0  8.0  10.0
      C  2.0  3.0  6.0  7.0   NaN
      D  NaN  NaN  NaN  NaN   9.0

### From a Series

The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided).

### From a list of namedtuples

The field names of the first `namedtuple` in the list determine the columns of the `DataFrame`. The remaining namedtuples (or tuples) are simply unpacked and their values are fed into the rows of the `DataFrame`. If any of those tuples is shorter than the first `namedtuple` then the later columns in the corresponding row are marked as missing values. If any are longer than the first `namedtuple`, a `ValueError` is raised.

    In [57]: from collections import namedtuple

    In [58]: Point = namedtuple("Point", "x y")

    In [59]: pd.DataFrame([Point(0, 0), Point(0, 3), (2, 3)])
    Out[59]:
       x  y
    0  0  0
    1  0  3
    2  2  3

    In [60]: Point3D = namedtuple("Point3D", "x y z")

    In [61]: pd.DataFrame([Point3D(0, 0, 0), Point3D(0, 3, 5), Point(2, 3)])
    Out[61]:
       x  y    z
    0  0  0  0.0
    1  0  3  5.0
    2  2  3  NaN

### From a list of dataclasses

New in version 1.1.0.

Data Classes as introduced in PEP557, can be passed into the DataFrame constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries.

Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a TypeError.

    In [62]: from dataclasses import make_dataclass

    In [63]: Point = make_dataclass("Point", [("x", int), ("y", int)])

    In [64]: pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
    Out[64]:
       x  y
    0  0  0
    1  0  3
    2  2  3

Missing data

Much more will be said on this topic in the Missing data section. To construct a DataFrame with missing data, we use `np.nan` to represent missing values. Alternatively, you may pass a `numpy.MaskedArray` as the data argument to the DataFrame constructor, and its masked entries will be considered missing.

### Alternate constructors

DataFrame.from_dict

`DataFrame.from_dict` takes a dict of dicts or a dict of array-like sequences and returns a DataFrame. It operates like the `DataFrame` constructor except for the `orient` parameter which is `'columns'` by default, but which can be set to `'index'` in order to use the dict keys as row labels.

    In [65]: pd.DataFrame.from_dict(dict([("A", [1, 2, 3]), ("B", [4, 5, 6])]))
    Out[65]:
       A  B
    0  1  4
    1  2  5
    2  3  6

If you pass `orient='index'`, the keys will be the row labels. In this case, you can also pass the desired column names:

    In [66]: pd.DataFrame.from_dict(
       ....:     dict([("A", [1, 2, 3]), ("B", [4, 5, 6])]),
       ....:     orient="index",
       ....:     columns=["one", "two", "three"],
       ....: )
       ....:
    Out[66]:
       one  two  three
    A    1    2      3
    B    4    5      6

DataFrame.from_records

`DataFrame.from_records` takes a list of tuples or an ndarray with structured dtype. It works analogously to the normal `DataFrame` constructor, except that the resulting DataFrame index may be a specific field of the structured dtype. For example:

    In [67]: data
    Out[67]:
    array([(1, 2., b'Hello'), (2, 3., b'World')],
          dtype=[('A', '<i4'), ('B', '<f4'), ('C', 'S10')])

    In [68]: pd.DataFrame.from_records(data, index="C")
    Out[68]:
              A    B
    C
    b'Hello'  1  2.0
    b'World'  2  3.0

### Column selection, addition, deletion

You can treat a DataFrame semantically like a dict of like-indexed Series objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:

    In [69]: df["one"]
    Out[69]:
    a    1.0
    b    2.0
    c    3.0
    d    NaN
    Name: one, dtype: float64

    In [70]: df["three"] = df["one"] * df["two"]

    In [71]: df["flag"] = df["one"] > 2

    In [72]: df
    Out[72]:
       one  two  three   flag
    a  1.0  1.0    1.0  False
    b  2.0  2.0    4.0  False
    c  3.0  3.0    9.0   True
    d  NaN  4.0    NaN  False

Columns can be deleted or popped like with a dict:

    In [73]: del df["two"]

    In [74]: three = df.pop("three")

    In [75]: df
    Out[75]:
       one   flag
    a  1.0  False
    b  2.0  False
    c  3.0   True
    d  NaN  False

When inserting a scalar value, it will naturally be propagated to fill the column:

    In [76]: df["foo"] = "bar"

    In [77]: df
    Out[77]:
       one   flag  foo
    a  1.0  False  bar
    b  2.0  False  bar
    c  3.0   True  bar
    d  NaN  False  bar

When inserting a Series that does not have the same index as the DataFrame, it will be conformed to the DataFrame’s index:

    In [78]: df["one_trunc"] = df["one"][:2]

    In [79]: df
    Out[79]:
       one   flag  foo  one_trunc
    a  1.0  False  bar        1.0
    b  2.0  False  bar        2.0
    c  3.0   True  bar        NaN
    d  NaN  False  bar        NaN

You can insert raw ndarrays but their length must match the length of the DataFrame’s index.

By default, columns get inserted at the end. The `insert` function is available to insert at a particular location in the columns:

    In [80]: df.insert(1, "bar", df["one"])

    In [81]: df
    Out[81]:
       one  bar   flag  foo  one_trunc
    a  1.0  1.0  False  bar        1.0
    b  2.0  2.0  False  bar        2.0
    c  3.0  3.0   True  bar        NaN
    d  NaN  NaN  False  bar        NaN

### Assigning new columns in method chains

Inspired by dplyr’s `mutate` verb, DataFrame has an `assign()` method that allows you to easily create new columns that are potentially derived from existing columns.

    In [82]: iris = pd.read_csv("data/iris.data")

    In [83]: iris.head()
    Out[83]:
       SepalLength  SepalWidth  PetalLength  PetalWidth         Name
    0          5.1         3.5          1.4         0.2  Iris-setosa
    1          4.9         3.0          1.4         0.2  Iris-setosa
    2          4.7         3.2          1.3         0.2  Iris-setosa
    3          4.6         3.1          1.5         0.2  Iris-setosa
    4          5.0         3.6          1.4         0.2  Iris-setosa

    In [84]: iris.assign(sepal_ratio=iris["SepalWidth"] / iris["SepalLength"]).head()
    Out[84]:
       SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio
    0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275
    1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245
    2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851
    3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913
    4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000

In the example above, we inserted a precomputed value. We can also pass in a function of one argument to be evaluated on the DataFrame being assigned to.

    In [85]: iris.assign(sepal_ratio=lambda x: (x["SepalWidth"] / x["SepalLength"])).head()
    Out[85]:
       SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio
    0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275
    1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245
    2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851
    3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913
    4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000

`assign` always returns a copy of the data, leaving the original DataFrame untouched.

Passing a callable, as opposed to an actual value to be inserted, is useful when you don’t have a reference to the DataFrame at hand. This is common when using `assign` in a chain of operations. For example, we can limit the DataFrame to just those observations with a Sepal Length greater than 5, calculate the ratio, and plot:

    In [86]: (
       ....:     iris.query("SepalLength > 5")
       ....:     .assign(
       ....:         SepalRatio=lambda x: x.SepalWidth / x.SepalLength,
       ....:         PetalRatio=lambda x: x.PetalWidth / x.PetalLength,
       ....:     )
       ....:     .plot(kind="scatter", x="SepalRatio", y="PetalRatio")
       ....: )
       ....:
    Out[86]: <AxesSubplot:xlabel='SepalRatio', ylabel='PetalRatio'>

Since a function is passed in, the function is computed on the DataFrame being assigned to. Importantly, this is the DataFrame that’s been filtered to those rows with sepal length greater than 5. The filtering happens first, and then the ratio calculations. This is an example where we didn’t have a reference to the filtered DataFrame available.

The function signature for `assign` is simply `**kwargs`. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a `Series` or NumPy array), or a function of one argument to be called on the `DataFrame`. A copy of the original DataFrame is returned, with the new values inserted.

Starting with Python 3.6 the order of `**kwargs` is preserved. This allows for dependent assignment, where an expression later in `**kwargs` can refer to a column created earlier in the same `assign()`.

    In [87]: dfa = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})

    In [88]: dfa.assign(C=lambda x: x["A"] + x["B"], D=lambda x: x["A"] + x["C"])
    Out[88]:
       A  B  C   D
    0  1  4  5   6
    1  2  5  7   9
    2  3  6  9  12

In the second expression, `x['C']` will refer to the newly created column, that’s equal to `dfa['A'] + dfa['B']`.

### Indexing / selection

The basics of indexing are as follows:

Operation | Syntax | Result
--- | --- | ---
Select column | `df[col]` | Series
Select row by label | `df.loc[label]` | Series
Select row by integer location | `df.iloc[loc]` | Series
Slice rows | `df[5:10]` | DataFrame
Select rows by boolean vector | `df[bool_vec]` | DataFrame

Row selection, for example, returns a Series whose index is the columns of the DataFrame:

    In [89]: df.loc["b"]
    Out[89]:
    one            2.0
    bar            2.0
    flag         False
    foo            bar
    one_trunc      2.0
    Name: b, dtype: object

    In [90]: df.iloc[2]
    Out[90]:
    one           3.0
    bar           3.0
    flag         True
    foo           bar
    one_trunc     NaN
    Name: c, dtype: object

For a more exhaustive treatment of sophisticated label-based indexing and slicing, see the section on indexing. We will address the fundamentals of reindexing / conforming to new sets of labels in the section on reindexing.

### Data alignment and arithmetic

Data alignment between DataFrame objects automatically align on both the columns and the index (row labels). Again, the resulting object will have the union of the column and row labels.

    In [91]: df = pd.DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])

    In [92]: df2 = pd.DataFrame(np.random.randn(7, 3), columns=["A", "B", "C"])

    In [93]: df + df2
    Out[93]:
              A         B         C   D
    0  0.045691 -0.014138  1.380871 NaN
    1 -0.955398 -1.501007  0.037181 NaN
    2 -0.662690  1.534833 -0.859691 NaN
    3 -2.452949  1.237274 -0.133712 NaN
    4  1.414490  1.951676 -2.320422 NaN
    5 -0.494922 -1.649727 -1.084601 NaN
    6 -1.047551 -0.748572 -0.805479 NaN
    7       NaN       NaN       NaN NaN
    8       NaN       NaN       NaN NaN
    9       NaN       NaN       NaN NaN

When doing an operation between DataFrame and Series, the default behavior is to align the Series index on the DataFrame columns, thus broadcasting row-wise. For example:

    In [94]: df - df.iloc[0]
    Out[94]:
              A         B         C         D
    0  0.000000  0.000000  0.000000  0.000000
    1 -1.359261 -0.248717 -0.453372 -1.754659
    2  0.253128  0.829678  0.010026 -1.991234
    3 -1.311128  0.054325 -1.724913 -1.620544
    4  0.573025  1.500742 -0.676070  1.367331
    5 -1.741248  0.781993 -1.241620 -2.053136
    6 -1.240774 -0.869551 -0.153282  0.000430
    7 -0.743894  0.411013 -0.929563 -0.282386
    8 -1.194921  1.320690  0.238224 -1.482644
    9  2.293786  1.856228  0.773289 -1.446531

For explicit control over the matching and broadcasting behavior, see the section on flexible binary operations.

Operations with scalars are just as you would expect:

    In [95]: df * 5 + 2
    Out[95]:
               A         B         C          D
    0   3.359299 -0.124862  4.835102   3.381160
    1  -3.437003 -1.368449  2.568242  -5.392133
    2   4.624938  4.023526  4.885230  -6.575010
    3  -3.196342  0.146766 -3.789461  -4.721559
    4   6.224426  7.378849  1.454750  10.217815
    5  -5.346940  3.785103 -1.373001  -6.884519
    6  -2.844569 -4.472618  4.068691   3.383309
    7  -0.360173  1.930201  0.187285   1.969232
    8  -2.615303  6.478587  6.026220  -4.032059
    9  14.828230  9.156280  8.701544  -3.851494

    In [96]: 1 / df
    Out[96]:
              A          B         C           D
    0  3.678365  -2.353094  1.763605    3.620145
    1 -0.919624  -1.484363  8.799067   -0.676395
    2  1.904807   2.470934  1.732964   -0.583090
    3 -0.962215  -2.697986 -0.863638   -0.743875
    4  1.183593   0.929567 -9.170108    0.608434
    5 -0.680555   2.800959 -1.482360   -0.562777
    6 -1.032084  -0.772485  2.416988    3.614523
    7 -2.118489 -71.634509 -2.758294 -162.507295
    8 -1.083352   1.116424  1.241860   -0.828904
    9  0.389765   0.698687  0.746097   -0.854483

    In [97]: df ** 4
    Out[97]:
               A             B         C             D
    0   0.005462  3.261689e-02  0.103370  5.822320e-03
    1   1.398165  2.059869e-01  0.000167  4.777482e+00
    2   0.075962  2.682596e-02  0.110877  8.650845e+00
    3   1.166571  1.887302e-02  1.797515  3.265879e+00
    4   0.509555  1.339298e+00  0.000141  7.297019e+00
    5   4.661717  1.624699e-02  0.207103  9.969092e+00
    6   0.881334  2.808277e+00  0.029302  5.858632e-03
    7   0.049647  3.797614e-08  0.017276  1.433866e-09
    8   0.725974  6.437005e-01  0.420446  2.118275e+00
    9  43.329821  4.196326e+00  3.227153  1.875802e+00

Boolean operators work as well:

    In [98]: df1 = pd.DataFrame({"a": [1, 0, 1], "b": [0, 1, 1]}, dtype=bool)

    In [99]: df2 = pd.DataFrame({"a": [0, 1, 1], "b": [1, 1, 0]}, dtype=bool)

    In [100]: df1 & df2
    Out[100]:
           a      b
    0  False  False
    1  False   True
    2   True  False

    In [101]: df1 | df2
    Out[101]:
          a     b
    0  True  True
    1  True  True
    2  True  True

    In [102]: df1 ^ df2
    Out[102]:
           a      b
    0   True   True
    1   True  False
    2  False   True

    In [103]: -df1
    Out[103]:
           a      b
    0  False   True
    1   True  False
    2  False  False

### Transposing

To transpose, access the `T` attribute (also the `transpose` function), similar to an ndarray:

    # only show the first 5 rows
    In [104]: df[:5].T
    Out[104]:
              0         1         2         3         4
    A  0.271860 -1.087401  0.524988 -1.039268  0.844885
    B -0.424972 -0.673690  0.404705 -0.370647  1.075770
    C  0.567020  0.113648  0.577046 -1.157892 -0.109050
    D  0.276232 -1.478427 -1.715002 -1.344312  1.643563

### DataFrame interoperability with NumPy functions

Elementwise NumPy ufuncs (log, exp, sqrt, …) and various other NumPy functions can be used with no issues on Series and DataFrame, assuming the data within are numeric:

    In [105]: np.exp(df)
    Out[105]:
               A         B         C         D
    0   1.312403  0.653788  1.763006  1.318154
    1   0.337092  0.509824  1.120358  0.227996
    2   1.690438  1.498861  1.780770  0.179963
    3   0.353713  0.690288  0.314148  0.260719
    4   2.327710  2.932249  0.896686  5.173571
    5   0.230066  1.429065  0.509360  0.169161
    6   0.379495  0.274028  1.512461  1.318720
    7   0.623732  0.986137  0.695904  0.993865
    8   0.397301  2.449092  2.237242  0.299269
    9  13.009059  4.183951  3.820223  0.310274

    In [106]: np.asarray(df)
    Out[106]:
    array([[ 0.2719, -0.425 ,  0.567 ,  0.2762],
           [-1.0874, -0.6737,  0.1136, -1.4784],
           [ 0.525 ,  0.4047,  0.577 , -1.715 ],
           [-1.0393, -0.3706, -1.1579, -1.3443],
           [ 0.8449,  1.0758, -0.109 ,  1.6436],
           [-1.4694,  0.357 , -0.6746, -1.7769],
           [-0.9689, -1.2945,  0.4137,  0.2767],
           [-0.472 , -0.014 , -0.3625, -0.0062],
           [-0.9231,  0.8957,  0.8052, -1.2064],
           [ 2.5656,  1.4313,  1.3403, -1.1703]])

DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array.

`Series` implements `__array_ufunc__`, which allows it to work with NumPy’s universal functions.

The ufunc is applied to the underlying array in a Series.

    In [107]: ser = pd.Series([1, 2, 3, 4])

    In [108]: np.exp(ser)
    Out[108]:
    0     2.718282
    1     7.389056
    2    20.085537
    3    54.598150
    dtype: float64

Changed in version 0.25.0: When multiple `Series` are passed to a ufunc, they are aligned before performing the operation.

Like other parts of the library, pandas will automatically align labeled inputs as part of a ufunc with multiple inputs. For example, using `numpy.remainder()` on two `Series` with differently ordered labels will align before the operation.

    In [109]: ser1 = pd.Series([1, 2, 3], index=["a", "b", "c"])

    In [110]: ser2 = pd.Series([1, 3, 5], index=["b", "a", "c"])

    In [111]: ser1
    Out[111]:
    a    1
    b    2
    c    3
    dtype: int64

    In [112]: ser2
    Out[112]:
    b    1
    a    3
    c    5
    dtype: int64

    In [113]: np.remainder(ser1, ser2)
    Out[113]:
    a    1
    b    0
    c    3
    dtype: int64

As usual, the union of the two indices is taken, and non-overlapping values are filled with missing values.

    In [114]: ser3 = pd.Series([2, 4, 6], index=["b", "c", "d"])

    In [115]: ser3
    Out[115]:
    b    2
    c    4
    d    6
    dtype: int64

    In [116]: np.remainder(ser1, ser3)
    Out[116]:
    a    NaN
    b    0.0
    c    3.0
    d    NaN
    dtype: float64

When a binary ufunc is applied to a `Series` and `Index`, the Series implementation takes precedence and a Series is returned.

    In [117]: ser = pd.Series([1, 2, 3])

    In [118]: idx = pd.Index([4, 5, 6])

    In [119]: np.maximum(ser, idx)
    Out[119]:
    0    4
    1    5
    2    6
    dtype: int64

NumPy ufuncs are safe to apply to `Series` backed by non-ndarray arrays, for example `arrays.SparseArray` (see Sparse calculation). If possible, the ufunc is applied without converting the underlying data to an ndarray.

### Console display

Very large DataFrames will be truncated to display them in the console. You can also get a summary using `info()`. (Here I am reading a CSV version of the baseball dataset from the plyr R package):

    In [120]: baseball = pd.read_csv("data/baseball.csv")

    In [121]: print(baseball)
           id     player  year  stint team  lg  ...    so  ibb  hbp   sh   sf  gidp
    0   88641  womacto01  2006      2  CHN  NL  ...   4.0  0.0  0.0  3.0  0.0   0.0
    1   88643  schilcu01  2006      1  BOS  AL  ...   1.0  0.0  0.0  0.0  0.0   0.0
    ..    ...        ...   ...    ...  ...  ..  ...   ...  ...  ...  ...  ...   ...
    98  89533   aloumo01  2007      1  NYN  NL  ...  30.0  5.0  2.0  0.0  3.0  13.0
    99  89534  alomasa02  2007      1  NYN  NL  ...   3.0  0.0  0.0  0.0  0.0   0.0

    [100 rows x 23 columns]

    In [122]: baseball.info()
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 100 entries, 0 to 99
    Data columns (total 23 columns):
     #   Column  Non-Null Count  Dtype
    ---  ------  --------------  -----
     0   id      100 non-null    int64
     1   player  100 non-null    object
     2   year    100 non-null    int64
     3   stint   100 non-null    int64
     4   team    100 non-null    object
     5   lg      100 non-null    object
     6   g       100 non-null    int64
     7   ab      100 non-null    int64
     8   r       100 non-null    int64
     9   h       100 non-null    int64
     10  X2b     100 non-null    int64
     11  X3b     100 non-null    int64
     12  hr      100 non-null    int64
     13  rbi     100 non-null    float64
     14  sb      100 non-null    float64
     15  cs      100 non-null    float64
     16  bb      100 non-null    int64
     17  so      100 non-null    float64
     18  ibb     100 non-null    float64
     19  hbp     100 non-null    float64
     20  sh      100 non-null    float64
     21  sf      100 non-null    float64
     22  gidp    100 non-null    float64
    dtypes: float64(9), int64(11), object(3)
    memory usage: 18.1+ KB

However, using `to_string` will return a string representation of the DataFrame in tabular form, though it won’t always fit the console width:

    In [123]: print(baseball.iloc[-20:, :12].to_string())
           id     player  year  stint team  lg    g   ab   r    h  X2b  X3b
    80  89474  finlest01  2007      1  COL  NL   43   94   9   17    3    0
    81  89480  embreal01  2007      1  OAK  AL    4    0   0    0    0    0
    82  89481  edmonji01  2007      1  SLN  NL  117  365  39   92   15    2
    83  89482  easleda01  2007      1  NYN  NL   76  193  24   54    6    0
    84  89489  delgaca01  2007      1  NYN  NL  139  538  71  139   30    0
    85  89493  cormirh01  2007      1  CIN  NL    6    0   0    0    0    0
    86  89494  coninje01  2007      2  NYN  NL   21   41   2    8    2    0
    87  89495  coninje01  2007      1  CIN  NL   80  215  23   57   11    1
    88  89497  clemero02  2007      1  NYA  AL    2    2   0    1    0    0
    89  89498  claytro01  2007      2  BOS  AL    8    6   1    0    0    0
    90  89499  claytro01  2007      1  TOR  AL   69  189  23   48   14    0
    91  89501  cirilje01  2007      2  ARI  NL   28   40   6    8    4    0
    92  89502  cirilje01  2007      1  MIN  AL   50  153  18   40    9    2
    93  89521  bondsba01  2007      1  SFN  NL  126  340  75   94   14    0
    94  89523  biggicr01  2007      1  HOU  NL  141  517  68  130   31    3
    95  89525  benitar01  2007      2  FLO  NL   34    0   0    0    0    0
    96  89526  benitar01  2007      1  SFN  NL   19    0   0    0    0    0
    97  89530  ausmubr01  2007      1  HOU  NL  117  349  38   82   16    3
    98  89533   aloumo01  2007      1  NYN  NL   87  328  51  112   19    1
    99  89534  alomasa02  2007      1  NYN  NL    8   22   1    3    1    0

Wide DataFrames will be printed across multiple rows by default:

    In [124]: pd.DataFrame(np.random.randn(3, 12))
    Out[124]:
             0         1         2   ...        9         10        11
    0 -1.226825  0.769804 -1.281247  ... -1.110336 -0.619976  0.149748
    1 -0.732339  0.687738  0.176444  ...  1.462696 -1.743161 -0.826591
    2 -0.345352  1.314232  0.690579  ...  0.896171 -0.487602 -0.082240

    [3 rows x 12 columns]

You can change how much to print on a single row by setting the `display.width` option:

    In [125]: pd.set_option("display.width", 40)  # default is 80

    In [126]: pd.DataFrame(np.random.randn(3, 12))
    Out[126]:
             0         1         2   ...        9         10        11
    0 -2.182937  0.380396  0.084844  ... -0.023688  2.410179  1.450520
    1  0.206053 -0.251905 -2.213588  ... -0.025747 -0.988387  0.094055
    2  1.262731  1.289997  0.082423  ... -0.281461  0.030711  0.109121

    [3 rows x 12 columns]

You can adjust the max width of the individual columns by setting `display.max_colwidth`

    In [127]: datafile = {
       .....:     "filename": ["filename_01", "filename_02"],
       .....:     "path": [
       .....:         "media/user_name/storage/folder_01/filename_01",
       .....:         "media/user_name/storage/folder_02/filename_02",
       .....:     ],
       .....: }
       .....:

    In [128]: pd.set_option("display.max_colwidth", 30)

    In [129]: pd.DataFrame(datafile)
    Out[129]:
          filename                           path
    0  filename_01  media/user_name/storage/fo...
    1  filename_02  media/user_name/storage/fo...

    In [130]: pd.set_option("display.max_colwidth", 100)

    In [131]: pd.DataFrame(datafile)
    Out[131]:
          filename                                           path
    0  filename_01  media/user_name/storage/folder_01/filename_01
    1  filename_02  media/user_name/storage/folder_02/filename_02

You can also disable this feature via the `expand_frame_repr` option. This will print the table in one block.

### DataFrame column attribute access and IPython completion

If a DataFrame column label is a valid Python variable name, the column can be accessed like an attribute:

    In [132]: df = pd.DataFrame({"foo1": np.random.randn(5), "foo2": np.random.randn(5)})

    In [133]: df
    Out[133]:
           foo1      foo2
    0  1.126203  0.781836
    1 -0.977349 -1.071357
    2  1.474071  0.441153
    3 -0.064034  2.353925
    4 -1.282782  0.583787

    In [134]: df.foo1
    Out[134]:
    0    1.126203
    1   -0.977349
    2    1.474071
    3   -0.064034
    4   -1.282782
    Name: foo1, dtype: float64

The columns are also connected to the IPython completion mechanism so they can be tab-completed:

    In [5]: df.foo<TAB>  # noqa: E225, E999
    df.foo1  df.foo2