.. _basics: {{ header }} ============================== Essential basic functionality ============================== Here we discuss a lot of the essential functionality common to the pandas data structures. To begin, let's create some example objects like we did in the :ref:`10 minutes to pandas <10min>` section: .. ipython:: python index = pd.date_range("1/1/2000", periods=8) s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"]) df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=["A", "B", "C"]) .. _basics.head_tail: Head and tail ------------- To view a small sample of a Series or DataFrame object, use the :meth:`~DataFrame.head` and :meth:`~DataFrame.tail` methods. The default number of elements to display is five, but you may pass a custom number. .. ipython:: python long_series = pd.Series(np.random.randn(1000)) long_series.head() long_series.tail(3) .. _basics.attrs: Attributes and underlying data ------------------------------ pandas objects have a number of attributes enabling you to access the metadata * **shape**: gives the axis dimensions of the object, consistent with ndarray * Axis labels * **Series**: *index* (only axis) * **DataFrame**: *index* (rows) and *columns* Note, **these attributes can be safely assigned to**! .. ipython:: python df[:2] df.columns = [x.lower() for x in df.columns] df pandas objects (:class:`Index`, :class:`Series`, :class:`DataFrame`) can be thought of as containers for arrays, which hold the actual data and do the actual computation. For many types, the underlying array is a :class:`numpy.ndarray`. However, pandas and 3rd party libraries may *extend* NumPy's type system to add support for custom arrays (see :ref:`basics.dtypes`). To get the actual data inside a :class:`Index` or :class:`Series`, use the ``.array`` property .. ipython:: python s.array s.index.array :attr:`~Series.array` will always be an :class:`~pandas.api.extensions.ExtensionArray`. The exact details of what an :class:`~pandas.api.extensions.ExtensionArray` is and why pandas uses them are a bit beyond the scope of this introduction. See :ref:`basics.dtypes` for more. If you know you need a NumPy array, use :meth:`~Series.to_numpy` or :meth:`numpy.asarray`. .. ipython:: python s.to_numpy() np.asarray(s) When the Series or Index is backed by an :class:`~pandas.api.extensions.ExtensionArray`, :meth:`~Series.to_numpy` may involve copying data and coercing values. See :ref:`basics.dtypes` for more. :meth:`~Series.to_numpy` gives some control over the ``dtype`` of the resulting :class:`numpy.ndarray`. For example, consider datetimes with timezones. NumPy doesn't have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations: 1. An object-dtype :class:`numpy.ndarray` with :class:`Timestamp` objects, each with the correct ``tz`` 2. A ``datetime64[ns]`` -dtype :class:`numpy.ndarray`, where the values have been converted to UTC and the timezone discarded Timezones may be preserved with ``dtype=object`` .. ipython:: python ser = pd.Series(pd.date_range("2000", periods=2, tz="CET")) ser.to_numpy(dtype=object) Or thrown away with ``dtype='datetime64[ns]'`` .. ipython:: python ser.to_numpy(dtype="datetime64[ns]") Getting the "raw data" inside a :class:`DataFrame` is possibly a bit more complex. When your ``DataFrame`` only has a single data type for all the columns, :meth:`DataFrame.to_numpy` will return the underlying data: .. ipython:: python df.to_numpy() If a DataFrame contains homogeneously-typed data, the ndarray can actually be modified in-place, and the changes will be reflected in the data structure. For heterogeneous data (e.g. some of the DataFrame's columns are not all the same dtype), this will not be the case. The values attribute itself, unlike the axis labels, cannot be assigned to. .. note:: When working with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved. For example, if strings are involved, the result will be of object dtype. If there are only floats and integers, the resulting array will be of float dtype. In the past, pandas recommended :attr:`Series.values` or :attr:`DataFrame.values` for extracting the data from a Series or DataFrame. You'll still find references to these in old code bases and online. Going forward, we recommend avoiding ``.values`` and using ``.array`` or ``.to_numpy()``. ``.values`` has the following drawbacks: 1. When your Series contains an :ref:`extension type `, it's unclear whether :attr:`Series.values` returns a NumPy array or the extension array. :attr:`Series.array` will always return an :class:`~pandas.api.extensions.ExtensionArray`, and will never copy data. :meth:`Series.to_numpy` will always return a NumPy array, potentially at the cost of copying / coercing values. 2. When your DataFrame contains a mixture of data types, :attr:`DataFrame.values` may involve copying data and coercing values to a common dtype, a relatively expensive operation. :meth:`DataFrame.to_numpy`, being a method, makes it clearer that the returned NumPy array may not be a view on the same data in the DataFrame. .. _basics.accelerate: Accelerated operations ---------------------- pandas has support for accelerating certain types of binary numerical and boolean operations using the ``numexpr`` library and the ``bottleneck`` libraries. These libraries are especially useful when dealing with large data sets, and provide large speedups. ``numexpr`` uses smart chunking, caching, and multiple cores. ``bottleneck`` is a set of specialized cython routines that are especially fast when dealing with arrays that have ``nans``. You are highly encouraged to install both libraries. See the section :ref:`Recommended Dependencies ` for more installation info. These are both enabled to be used by default, you can control this by setting the options: .. code-block:: python pd.set_option("compute.use_bottleneck", False) pd.set_option("compute.use_numexpr", False) .. _basics.binop: Flexible binary operations -------------------------- With binary operations between pandas data structures, there are two key points of interest: * Broadcasting behavior between higher- (e.g. DataFrame) and lower-dimensional (e.g. Series) objects. * Missing data in computations. We will demonstrate how to manage these issues independently, though they can be handled simultaneously. Matching / broadcasting behavior ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DataFrame has the methods :meth:`~DataFrame.add`, :meth:`~DataFrame.sub`, :meth:`~DataFrame.mul`, :meth:`~DataFrame.div` and related functions :meth:`~DataFrame.radd`, :meth:`~DataFrame.rsub`, ... for carrying out binary operations. For broadcasting behavior, Series input is of primary interest. Using these functions, you can use to either match on the *index* or *columns* via the **axis** keyword: .. ipython:: python df = pd.DataFrame( { "one": pd.Series(np.random.randn(3), index=["a", "b", "c"]), "two": pd.Series(np.random.randn(4), index=["a", "b", "c", "d"]), "three": pd.Series(np.random.randn(3), index=["b", "c", "d"]), } ) df row = df.iloc[1] column = df["two"] df.sub(row, axis="columns") df.sub(row, axis=1) df.sub(column, axis="index") df.sub(column, axis=0) Furthermore you can align a level of a MultiIndexed DataFrame with a Series. .. ipython:: python dfmi = df.copy() dfmi.index = pd.MultiIndex.from_tuples( [(1, "a"), (1, "b"), (1, "c"), (2, "a")], names=["first", "second"] ) dfmi.sub(column, axis=0, level="second") Series and Index also support the :func:`divmod` builtin. This function takes the floor division and modulo operation at the same time returning a two-tuple of the same type as the left hand side. For example: .. ipython:: python s = pd.Series(np.arange(10)) s div, rem = divmod(s, 3) div rem idx = pd.Index(np.arange(10)) idx div, rem = divmod(idx, 3) div rem We can also do elementwise :func:`divmod`: .. ipython:: python div, rem = divmod(s, [2, 2, 3, 3, 4, 4, 5, 5, 6, 6]) div rem Missing data / operations with fill values ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In Series and DataFrame, the arithmetic functions have the option of inputting a *fill_value*, namely a value to substitute when at most one of the values at a location are missing. For example, when adding two DataFrame objects, you may wish to treat NaN as 0 unless both DataFrames are missing that value, in which case the result will be NaN (you can later replace NaN with some other value using ``fillna`` if you wish). .. ipython:: python df2 = df.copy() df2.loc["a", "three"] = 1.0 df df2 df + df2 df.add(df2, fill_value=0) .. _basics.compare: Flexible comparisons ~~~~~~~~~~~~~~~~~~~~ Series and DataFrame have the binary comparison methods ``eq``, ``ne``, ``lt``, ``gt``, ``le``, and ``ge`` whose behavior is analogous to the binary arithmetic operations described above: .. ipython:: python df.gt(df2) df2.ne(df) These operations produce a pandas object of the same type as the left-hand-side input that is of dtype ``bool``. These ``boolean`` objects can be used in indexing operations, see the section on :ref:`Boolean indexing`. .. _basics.reductions: Boolean reductions ~~~~~~~~~~~~~~~~~~ You can apply the reductions: :attr:`~DataFrame.empty`, :meth:`~DataFrame.any`, :meth:`~DataFrame.all`. .. ipython:: python (df > 0).all() (df > 0).any() You can reduce to a final boolean value. .. ipython:: python (df > 0).any().any() You can test if a pandas object is empty, via the :attr:`~DataFrame.empty` property. .. ipython:: python df.empty pd.DataFrame(columns=list("ABC")).empty .. warning:: Asserting the truthiness of a pandas object will raise an error, as the testing of the emptiness or values is ambiguous. .. ipython:: python :okexcept: if df: print(True) .. ipython:: python :okexcept: df and df2 See :ref:`gotchas` for a more detailed discussion. .. _basics.equals: Comparing if objects are equivalent ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Often you may find that there is more than one way to compute the same result. As a simple example, consider ``df + df`` and ``df * 2``. To test that these two computations produce the same result, given the tools shown above, you might imagine using ``(df + df == df * 2).all()``. But in fact, this expression is False: .. ipython:: python df + df == df * 2 (df + df == df * 2).all() Notice that the boolean DataFrame ``df + df == df * 2`` contains some False values! This is because NaNs do not compare as equals: .. ipython:: python np.nan == np.nan So, NDFrames (such as Series and DataFrames) have an :meth:`~DataFrame.equals` method for testing equality, with NaNs in corresponding locations treated as equal. .. ipython:: python (df + df).equals(df * 2) Note that the Series or DataFrame index needs to be in the same order for equality to be True: .. ipython:: python df1 = pd.DataFrame({"col": ["foo", 0, np.nan]}) df2 = pd.DataFrame({"col": [np.nan, 0, "foo"]}, index=[2, 1, 0]) df1.equals(df2) df1.equals(df2.sort_index()) Comparing array-like objects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You can conveniently perform element-wise comparisons when comparing a pandas data structure with a scalar value: .. ipython:: python pd.Series(["foo", "bar", "baz"]) == "foo" pd.Index(["foo", "bar", "baz"]) == "foo" pandas also handles element-wise comparisons between different array-like objects of the same length: .. ipython:: python pd.Series(["foo", "bar", "baz"]) == pd.Index(["foo", "bar", "qux"]) pd.Series(["foo", "bar", "baz"]) == np.array(["foo", "bar", "qux"]) Trying to compare ``Index`` or ``Series`` objects of different lengths will raise a ValueError: .. ipython:: python :okexcept: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar']) pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo']) Combining overlapping data sets ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A problem occasionally arising is the combination of two similar data sets where values in one are preferred over the other. An example would be two data series representing a particular economic indicator where one is considered to be of "higher quality". However, the lower quality series might extend further back in history or have more complete data coverage. As such, we would like to combine two DataFrame objects where missing values in one DataFrame are conditionally filled with like-labeled values from the other DataFrame. The function implementing this operation is :meth:`~DataFrame.combine_first`, which we illustrate: .. ipython:: python df1 = pd.DataFrame( {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]} ) df2 = pd.DataFrame( { "A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0], "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0], } ) df1 df2 df1.combine_first(df2) General DataFrame combine ~~~~~~~~~~~~~~~~~~~~~~~~~ The :meth:`~DataFrame.combine_first` method above calls the more general :meth:`DataFrame.combine`. This method takes another DataFrame and a combiner function, aligns the input DataFrame and then passes the combiner function pairs of Series (i.e., columns whose names are the same). So, for instance, to reproduce :meth:`~DataFrame.combine_first` as above: .. ipython:: python def combiner(x, y): return np.where(pd.isna(x), y, x) df1.combine(df2, combiner) .. _basics.stats: Descriptive statistics ---------------------- There exists a large number of methods for computing descriptive statistics and other related operations on :ref:`Series `, :ref:`DataFrame `. Most of these are aggregations (hence producing a lower-dimensional result) like :meth:`~DataFrame.sum`, :meth:`~DataFrame.mean`, and :meth:`~DataFrame.quantile`, but some of them, like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.cumprod`, produce an object of the same size. Generally speaking, these methods take an **axis** argument, just like *ndarray.{sum, std, ...}*, but the axis can be specified by name or integer: * **Series**: no axis argument needed * **DataFrame**: "index" (axis=0, default), "columns" (axis=1) For example: .. ipython:: python df df.mean(axis=0) df.mean(axis=1) All such methods have a ``skipna`` option signaling whether to exclude missing data (``True`` by default): .. ipython:: python df.sum(axis=0, skipna=False) df.sum(axis=1, skipna=True) Combined with the broadcasting / arithmetic behavior, one can describe various statistical procedures, like standardization (rendering data zero mean and standard deviation of 1), very concisely: .. ipython:: python ts_stand = (df - df.mean()) / df.std() ts_stand.std() xs_stand = df.sub(df.mean(axis=1), axis=0).div(df.std(axis=1), axis=0) xs_stand.std(axis=1) Note that methods like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.cumprod` preserve the location of ``NaN`` values. This is somewhat different from :meth:`~DataFrame.expanding` and :meth:`~DataFrame.rolling` since ``NaN`` behavior is furthermore dictated by a ``min_periods`` parameter. .. ipython:: python df.cumsum() Here is a quick reference summary table of common functions. Each also takes an optional ``level`` parameter which applies only if the object has a :ref:`hierarchical index`. .. csv-table:: :header: "Function", "Description" :widths: 20, 80 ``count``, Number of non-NA observations ``sum``, Sum of values ``mean``, Mean of values ``median``, Arithmetic median of values ``min``, Minimum ``max``, Maximum ``mode``, Mode ``abs``, Absolute Value ``prod``, Product of values ``std``, Bessel-corrected sample standard deviation ``var``, Unbiased variance ``sem``, Standard error of the mean ``skew``, Sample skewness (3rd moment) ``kurt``, Sample kurtosis (4th moment) ``quantile``, Sample quantile (value at %) ``cumsum``, Cumulative sum ``cumprod``, Cumulative product ``cummax``, Cumulative maximum ``cummin``, Cumulative minimum Note that by chance some NumPy methods, like ``mean``, ``std``, and ``sum``, will exclude NAs on Series input by default: .. ipython:: python np.mean(df["one"]) np.mean(df["one"].to_numpy()) :meth:`Series.nunique` will return the number of unique non-NA values in a Series: .. ipython:: python series = pd.Series(np.random.randn(500)) series[20:500] = np.nan series[10:20] = 5 series.nunique() .. _basics.describe: Summarizing data: describe ~~~~~~~~~~~~~~~~~~~~~~~~~~ There is a convenient :meth:`~DataFrame.describe` function which computes a variety of summary statistics about a Series or the columns of a DataFrame (excluding NAs of course): .. ipython:: python series = pd.Series(np.random.randn(1000)) series[::2] = np.nan series.describe() frame = pd.DataFrame(np.random.randn(1000, 5), columns=["a", "b", "c", "d", "e"]) frame.iloc[::2] = np.nan frame.describe() You can select specific percentiles to include in the output: .. ipython:: python series.describe(percentiles=[0.05, 0.25, 0.75, 0.95]) By default, the median is always included. For a non-numerical Series object, :meth:`~Series.describe` will give a simple summary of the number of unique values and most frequently occurring values: .. ipython:: python s = pd.Series(["a", "a", "b", "b", "a", "a", np.nan, "c", "d", "a"]) s.describe() Note that on a mixed-type DataFrame object, :meth:`~DataFrame.describe` will restrict the summary to include only numerical columns or, if none are, only categorical columns: .. ipython:: python frame = pd.DataFrame({"a": ["Yes", "Yes", "No", "No"], "b": range(4)}) frame.describe() This behavior can be controlled by providing a list of types as ``include``/``exclude`` arguments. The special value ``all`` can also be used: .. ipython:: python frame.describe(include=["object"]) frame.describe(include=["number"]) frame.describe(include="all") That feature relies on :ref:`select_dtypes `. Refer to there for details about accepted inputs. .. _basics.idxmin: Index of min/max values ~~~~~~~~~~~~~~~~~~~~~~~ The :meth:`~DataFrame.idxmin` and :meth:`~DataFrame.idxmax` functions on Series and DataFrame compute the index labels with the minimum and maximum corresponding values: .. ipython:: python s1 = pd.Series(np.random.randn(5)) s1 s1.idxmin(), s1.idxmax() df1 = pd.DataFrame(np.random.randn(5, 3), columns=["A", "B", "C"]) df1 df1.idxmin(axis=0) df1.idxmax(axis=1) When there are multiple rows (or columns) matching the minimum or maximum value, :meth:`~DataFrame.idxmin` and :meth:`~DataFrame.idxmax` return the first matching index: .. ipython:: python df3 = pd.DataFrame([2, 1, 1, 3, np.nan], columns=["A"], index=list("edcba")) df3 df3["A"].idxmin() .. note:: ``idxmin`` and ``idxmax`` are called ``argmin`` and ``argmax`` in NumPy. .. _basics.discretization: Value counts (histogramming) / mode ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The :meth:`~Series.value_counts` Series method computes a histogram of a 1D array of values. It can also be used as a function on regular arrays: .. ipython:: python data = np.random.randint(0, 7, size=50) data s = pd.Series(data) s.value_counts() The :meth:`~DataFrame.value_counts` method can be used to count combinations across multiple columns. By default all columns are used but a subset can be selected using the ``subset`` argument. .. ipython:: python data = {"a": [1, 2, 3, 4], "b": ["x", "x", "y", "y"]} frame = pd.DataFrame(data) frame.value_counts() Similarly, you can get the most frequently occurring value(s), i.e. the mode, of the values in a Series or DataFrame: .. ipython:: python s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7]) s5.mode() df5 = pd.DataFrame( { "A": np.random.randint(0, 7, size=50), "B": np.random.randint(-10, 15, size=50), } ) df5.mode() Discretization and quantiling ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Continuous values can be discretized using the :func:`cut` (bins based on values) and :func:`qcut` (bins based on sample quantiles) functions: .. ipython:: python arr = np.random.randn(20) factor = pd.cut(arr, 4) factor factor = pd.cut(arr, [-5, -1, 0, 1, 5]) factor :func:`qcut` computes sample quantiles. For example, we could slice up some normally distributed data into equal-size quartiles like so: .. ipython:: python arr = np.random.randn(30) factor = pd.qcut(arr, [0, 0.25, 0.5, 0.75, 1]) factor We can also pass infinite values to define the bins: .. ipython:: python arr = np.random.randn(20) factor = pd.cut(arr, [-np.inf, 0, np.inf]) factor .. _basics.apply: Function application -------------------- To apply your own or another library's functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects to operate on an entire ``DataFrame`` or ``Series``, row- or column-wise, or elementwise. 1. `Tablewise Function Application`_: :meth:`~DataFrame.pipe` 2. `Row or Column-wise Function Application`_: :meth:`~DataFrame.apply` 3. `Aggregation API`_: :meth:`~DataFrame.agg` and :meth:`~DataFrame.transform` 4. `Applying Elementwise Functions`_: :meth:`~DataFrame.map` .. _basics.pipe: Tablewise function application ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``DataFrames`` and ``Series`` can be passed into functions. However, if the function needs to be called in a chain, consider using the :meth:`~DataFrame.pipe` method. First some setup: .. ipython:: python def extract_city_name(df): """ Chicago, IL -> Chicago for city_name column """ df["city_name"] = df["city_and_code"].str.split(",").str.get(0) return df def add_country_name(df, country_name=None): """ Chicago -> Chicago-US for city_name column """ col = "city_name" df["city_and_country"] = df[col] + country_name return df df_p = pd.DataFrame({"city_and_code": ["Chicago, IL"]}) ``extract_city_name`` and ``add_country_name`` are functions taking and returning ``DataFrames``. Now compare the following: .. ipython:: python add_country_name(extract_city_name(df_p), country_name="US") Is equivalent to: .. ipython:: python df_p.pipe(extract_city_name).pipe(add_country_name, country_name="US") pandas encourages the second style, which is known as method chaining. ``pipe`` makes it easy to use your own or another library's functions in method chains, alongside pandas' methods. In the example above, the functions ``extract_city_name`` and ``add_country_name`` each expected a ``DataFrame`` as the first positional argument. What if the function you wish to apply takes its data as, say, the second argument? In this case, provide ``pipe`` with a tuple of ``(callable, data_keyword)``. ``.pipe`` will route the ``DataFrame`` to the argument specified in the tuple. For example, we can fit a regression using statsmodels. Their API expects a formula first and a ``DataFrame`` as the second argument, ``data``. We pass in the function, keyword pair ``(sm.ols, 'data')`` to ``pipe``: .. code-block:: ipython In [147]: import statsmodels.formula.api as sm In [148]: bb = pd.read_csv("data/baseball.csv", index_col="id") In [149]: ( .....: bb.query("h > 0") .....: .assign(ln_h=lambda df: np.log(df.h)) .....: .pipe((sm.ols, "data"), "hr ~ ln_h + year + g + C(lg)") .....: .fit() .....: .summary() .....: ) .....: Out[149]: """ OLS Regression Results ============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Tue, 22 Nov 2022 Prob (F-statistic): 3.48e-15 Time: 05:34:17 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust =============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------- Intercept -8484.7720 4664.146 -1.819 0.074 -1.78e+04 835.780 C(lg)[T.NL] -2.2736 1.325 -1.716 0.091 -4.922 0.375 ln_h -1.3542 0.875 -1.547 0.127 -3.103 0.395 year 4.2277 2.324 1.819 0.074 -0.417 8.872 g 0.1841 0.029 6.258 0.000 0.125 0.243 ============================================================================== Omnibus: 10.875 Durbin-Watson: 1.999 Prob(Omnibus): 0.004 Jarque-Bera (JB): 17.298 Skew: 0.537 Prob(JB): 0.000175 Kurtosis: 5.225 Cond. No. 1.49e+07 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 1.49e+07. This might indicate that there are strong multicollinearity or other numerical problems. """ The pipe method is inspired by unix pipes and more recently dplyr_ and magrittr_, which have introduced the popular ``(%>%)`` (read pipe) operator for R_. The implementation of ``pipe`` here is quite clean and feels right at home in Python. We encourage you to view the source code of :meth:`~DataFrame.pipe`. .. _dplyr: https://github.com/tidyverse/dplyr .. _magrittr: https://github.com/tidyverse/magrittr .. _R: https://www.r-project.org Row or column-wise function application ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Arbitrary functions can be applied along the axes of a DataFrame using the :meth:`~DataFrame.apply` method, which, like the descriptive statistics methods, takes an optional ``axis`` argument: .. ipython:: python df.apply(lambda x: np.mean(x)) df.apply(lambda x: np.mean(x), axis=1) df.apply(lambda x: x.max() - x.min()) df.apply(np.cumsum) df.apply(np.exp) The :meth:`~DataFrame.apply` method will also dispatch on a string method name. .. ipython:: python df.apply("mean") df.apply("mean", axis=1) The return type of the function passed to :meth:`~DataFrame.apply` affects the type of the final output from ``DataFrame.apply`` for the default behaviour: * If the applied function returns a ``Series``, the final output is a ``DataFrame``. The columns match the index of the ``Series`` returned by the applied function. * If the applied function returns any other type, the final output is a ``Series``. This default behaviour can be overridden using the ``result_type``, which accepts three options: ``reduce``, ``broadcast``, and ``expand``. These will determine how list-likes return values expand (or not) to a ``DataFrame``. :meth:`~DataFrame.apply` combined with some cleverness can be used to answer many questions about a data set. For example, suppose we wanted to extract the date where the maximum value for each column occurred: .. ipython:: python tsdf = pd.DataFrame( np.random.randn(1000, 3), columns=["A", "B", "C"], index=pd.date_range("1/1/2000", periods=1000), ) tsdf.apply(lambda x: x.idxmax()) You may also pass additional arguments and keyword arguments to the :meth:`~DataFrame.apply` method. .. ipython:: python def subtract_and_divide(x, sub, divide=1): return (x - sub) / divide df_udf = pd.DataFrame(np.ones((2, 2))) df_udf.apply(subtract_and_divide, args=(5,), divide=3) Another useful feature is the ability to pass Series methods to carry out some Series operation on each column or row: .. ipython:: python tsdf = pd.DataFrame( np.random.randn(10, 3), columns=["A", "B", "C"], index=pd.date_range("1/1/2000", periods=10), ) tsdf.iloc[3:7] = np.nan tsdf tsdf.apply(pd.Series.interpolate) Finally, :meth:`~DataFrame.apply` takes an argument ``raw`` which is False by default, which converts each row or column into a Series before applying the function. When set to True, the passed function will instead receive an ndarray object, which has positive performance implications if you do not need the indexing functionality. .. _basics.aggregate: Aggregation API ~~~~~~~~~~~~~~~ The aggregation API allows one to express possibly multiple aggregation operations in a single concise way. This API is similar across pandas objects, see :ref:`groupby API `, the :ref:`window API `, and the :ref:`resample API `. The entry point for aggregation is :meth:`DataFrame.aggregate`, or the alias :meth:`DataFrame.agg`. We will use a similar starting frame from above: .. ipython:: python tsdf = pd.DataFrame( np.random.randn(10, 3), columns=["A", "B", "C"], index=pd.date_range("1/1/2000", periods=10), ) tsdf.iloc[3:7] = np.nan tsdf Using a single function is equivalent to :meth:`~DataFrame.apply`. You can also pass named methods as strings. These will return a ``Series`` of the aggregated output: .. ipython:: python tsdf.agg(lambda x: np.sum(x)) tsdf.agg("sum") # these are equivalent to a ``.sum()`` because we are aggregating # on a single function tsdf.sum() Single aggregations on a ``Series`` this will return a scalar value: .. ipython:: python tsdf["A"].agg("sum") Aggregating with multiple functions +++++++++++++++++++++++++++++++++++ You can pass multiple aggregation arguments as a list. The results of each of the passed functions will be a row in the resulting ``DataFrame``. These are naturally named from the aggregation function. .. ipython:: python tsdf.agg(["sum"]) Multiple functions yield multiple rows: .. ipython:: python tsdf.agg(["sum", "mean"]) On a ``Series``, multiple functions return a ``Series``, indexed by the function names: .. ipython:: python tsdf["A"].agg(["sum", "mean"]) Passing a ``lambda`` function will yield a ```` named row: .. ipython:: python tsdf["A"].agg(["sum", lambda x: x.mean()]) Passing a named function will yield that name for the row: .. ipython:: python def mymean(x): return x.mean() tsdf["A"].agg(["sum", mymean]) Aggregating with a dict +++++++++++++++++++++++ Passing a dictionary of column names to a scalar or a list of scalars, to ``DataFrame.agg`` allows you to customize which functions are applied to which columns. Note that the results are not in any particular order, you can use an ``OrderedDict`` instead to guarantee ordering. .. ipython:: python tsdf.agg({"A": "mean", "B": "sum"}) Passing a list-like will generate a ``DataFrame`` output. You will get a matrix-like output of all of the aggregators. The output will consist of all unique functions. Those that are not noted for a particular column will be ``NaN``: .. ipython:: python tsdf.agg({"A": ["mean", "min"], "B": "sum"}) .. _basics.aggregation.custom_describe: Custom describe +++++++++++++++ With ``.agg()`` it is possible to easily create a custom describe function, similar to the built in :ref:`describe function `. .. ipython:: python from functools import partial q_25 = partial(pd.Series.quantile, q=0.25) q_25.__name__ = "25%" q_75 = partial(pd.Series.quantile, q=0.75) q_75.__name__ = "75%" tsdf.agg(["count", "mean", "std", "min", q_25, "median", q_75, "max"]) .. _basics.transform: Transform API ~~~~~~~~~~~~~ The :meth:`~DataFrame.transform` method returns an object that is indexed the same (same size) as the original. This API allows you to provide *multiple* operations at the same time rather than one-by-one. Its API is quite similar to the ``.agg`` API. We create a frame similar to the one used in the above sections. .. ipython:: python tsdf = pd.DataFrame( np.random.randn(10, 3), columns=["A", "B", "C"], index=pd.date_range("1/1/2000", periods=10), ) tsdf.iloc[3:7] = np.nan tsdf Transform the entire frame. ``.transform()`` allows input functions as: a NumPy function, a string function name or a user defined function. .. ipython:: python :okwarning: tsdf.transform(np.abs) tsdf.transform("abs") tsdf.transform(lambda x: x.abs()) Here :meth:`~DataFrame.transform` received a single function; this is equivalent to a `ufunc `__ application. .. ipython:: python np.abs(tsdf) Passing a single function to ``.transform()`` with a ``Series`` will yield a single ``Series`` in return. .. ipython:: python tsdf["A"].transform(np.abs) Transform with multiple functions +++++++++++++++++++++++++++++++++ Passing multiple functions will yield a column MultiIndexed DataFrame. The first level will be the original frame column names; the second level will be the names of the transforming functions. .. ipython:: python tsdf.transform([np.abs, lambda x: x + 1]) Passing multiple functions to a Series will yield a DataFrame. The resulting column names will be the transforming functions. .. ipython:: python tsdf["A"].transform([np.abs, lambda x: x + 1]) Transforming with a dict ++++++++++++++++++++++++ Passing a dict of functions will allow selective transforming per column. .. ipython:: python tsdf.transform({"A": np.abs, "B": lambda x: x + 1}) Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms. .. ipython:: python :okwarning: tsdf.transform({"A": np.abs, "B": [lambda x: x + 1, "sqrt"]}) .. _basics.elementwise: Applying elementwise functions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods :meth:`~DataFrame.map` on DataFrame and analogously :meth:`~Series.map` on Series accept any Python function taking a single value and returning a single value. For example: .. ipython:: python df4 = df.copy() df4 def f(x): return len(str(x)) df4["one"].map(f) df4.map(f) :meth:`Series.map` has an additional feature; it can be used to easily "link" or "map" values defined by a secondary series. This is closely related to :ref:`merging/joining functionality `: .. ipython:: python s = pd.Series( ["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"] ) t = pd.Series({"six": 6.0, "seven": 7.0}) s s.map(t) .. _basics.reindexing: Reindexing and altering labels ------------------------------ :meth:`~Series.reindex` is the fundamental data alignment method in pandas. It is used to implement nearly all other features relying on label-alignment functionality. To *reindex* means to conform the data to match a given set of labels along a particular axis. This accomplishes several things: * Reorders the existing data to match a new set of labels * Inserts missing value (NA) markers in label locations where no data for that label existed * If specified, **fill** data for missing labels using logic (highly relevant to working with time series data) Here is a simple example: .. ipython:: python s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"]) s s.reindex(["e", "b", "f", "d"]) Here, the ``f`` label was not contained in the Series and hence appears as ``NaN`` in the result. With a DataFrame, you can simultaneously reindex the index and columns: .. ipython:: python df df.reindex(index=["c", "f", "b"], columns=["three", "two", "one"]) Note that the ``Index`` objects containing the actual axis labels can be **shared** between objects. So if we have a Series and a DataFrame, the following can be done: .. ipython:: python rs = s.reindex(df.index) rs rs.index is df.index This means that the reindexed Series's index is the same Python object as the DataFrame's index. :meth:`DataFrame.reindex` also supports an "axis-style" calling convention, where you specify a single ``labels`` argument and the ``axis`` it applies to. .. ipython:: python df.reindex(["c", "f", "b"], axis="index") df.reindex(["three", "two", "one"], axis="columns") .. seealso:: :ref:`MultiIndex / Advanced Indexing ` is an even more concise way of doing reindexing. .. note:: When writing performance-sensitive code, there is a good reason to spend some time becoming a reindexing ninja: **many operations are faster on pre-aligned data**. Adding two unaligned DataFrames internally triggers a reindexing step. For exploratory analysis you will hardly notice the difference (because ``reindex`` has been heavily optimized), but when CPU cycles matter sprinkling a few explicit ``reindex`` calls here and there can have an impact. .. _basics.reindex_like: Reindexing to align with another object ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You may wish to take an object and reindex its axes to be labeled the same as another object. While the syntax for this is straightforward albeit verbose, it is a common enough operation that the :meth:`~DataFrame.reindex_like` method is available to make this simpler: .. ipython:: python df2 = df.reindex(["a", "b", "c"], columns=["one", "two"]) df3 = df2 - df2.mean() df2 df3 df.reindex_like(df2) .. _basics.align: Aligning objects with each other with ``align`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The :meth:`~Series.align` method is the fastest way to simultaneously align two objects. It supports a ``join`` argument (related to :ref:`joining and merging `): - ``join='outer'``: take the union of the indexes (default) - ``join='left'``: use the calling object's index - ``join='right'``: use the passed object's index - ``join='inner'``: intersect the indexes It returns a tuple with both of the reindexed Series: .. ipython:: python s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"]) s1 = s[:4] s2 = s[1:] s1.align(s2) s1.align(s2, join="inner") s1.align(s2, join="left") .. _basics.df_join: For DataFrames, the join method will be applied to both the index and the columns by default: .. ipython:: python df.align(df2, join="inner") You can also pass an ``axis`` option to only align on the specified axis: .. ipython:: python df.align(df2, join="inner", axis=0) .. _basics.align.frame.series: If you pass a Series to :meth:`DataFrame.align`, you can choose to align both objects either on the DataFrame's index or columns using the ``axis`` argument: .. ipython:: python df.align(df2.iloc[0], axis=1) .. _basics.reindex_fill: Filling while reindexing ~~~~~~~~~~~~~~~~~~~~~~~~ :meth:`~Series.reindex` takes an optional parameter ``method`` which is a filling method chosen from the following table: .. csv-table:: :header: "Method", "Action" :widths: 30, 50 ffill, Fill values forward bfill, Fill values backward nearest, Fill from the nearest index value We illustrate these fill methods on a simple Series: .. ipython:: python rng = pd.date_range("1/3/2000", periods=8) ts = pd.Series(np.random.randn(8), index=rng) ts2 = ts.iloc[[0, 3, 6]] ts ts2 ts2.reindex(ts.index) ts2.reindex(ts.index, method="ffill") ts2.reindex(ts.index, method="bfill") ts2.reindex(ts.index, method="nearest") These methods require that the indexes are **ordered** increasing or decreasing. Note that the same result could have been achieved using :ref:`ffill ` (except for ``method='nearest'``) or :ref:`interpolate `: .. ipython:: python ts2.reindex(ts.index).ffill() :meth:`~Series.reindex` will raise a ValueError if the index is not monotonically increasing or decreasing. :meth:`~Series.fillna` and :meth:`~Series.interpolate` will not perform any checks on the order of the index. .. _basics.limits_on_reindex_fill: Limits on filling while reindexing ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The ``limit`` and ``tolerance`` arguments provide additional control over filling while reindexing. Limit specifies the maximum count of consecutive matches: .. ipython:: python ts2.reindex(ts.index, method="ffill", limit=1) In contrast, tolerance specifies the maximum distance between the index and indexer values: .. ipython:: python ts2.reindex(ts.index, method="ffill", tolerance="1 day") Notice that when used on a ``DatetimeIndex``, ``TimedeltaIndex`` or ``PeriodIndex``, ``tolerance`` will coerced into a ``Timedelta`` if possible. This allows you to specify tolerance with appropriate strings. .. _basics.drop: Dropping labels from an axis ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A method closely related to ``reindex`` is the :meth:`~DataFrame.drop` function. It removes a set of labels from an axis: .. ipython:: python df df.drop(["a", "d"], axis=0) df.drop(["one"], axis=1) Note that the following also works, but is a bit less obvious / clean: .. ipython:: python df.reindex(df.index.difference(["a", "d"])) .. _basics.rename: Renaming / mapping labels ~~~~~~~~~~~~~~~~~~~~~~~~~ The :meth:`~DataFrame.rename` method allows you to relabel an axis based on some mapping (a dict or Series) or an arbitrary function. .. ipython:: python s s.rename(str.upper) If you pass a function, it must return a value when called with any of the labels (and must produce a set of unique values). A dict or Series can also be used: .. ipython:: python df.rename( columns={"one": "foo", "two": "bar"}, index={"a": "apple", "b": "banana", "d": "durian"}, ) If the mapping doesn't include a column/index label, it isn't renamed. Note that extra labels in the mapping don't throw an error. :meth:`DataFrame.rename` also supports an "axis-style" calling convention, where you specify a single ``mapper`` and the ``axis`` to apply that mapping to. .. ipython:: python df.rename({"one": "foo", "two": "bar"}, axis="columns") df.rename({"a": "apple", "b": "banana", "d": "durian"}, axis="index") Finally, :meth:`~Series.rename` also accepts a scalar or list-like for altering the ``Series.name`` attribute. .. ipython:: python s.rename("scalar-name") .. _basics.rename_axis: The methods :meth:`DataFrame.rename_axis` and :meth:`Series.rename_axis` allow specific names of a ``MultiIndex`` to be changed (as opposed to the labels). .. ipython:: python df = pd.DataFrame( {"x": [1, 2, 3, 4, 5, 6], "y": [10, 20, 30, 40, 50, 60]}, index=pd.MultiIndex.from_product( [["a", "b", "c"], [1, 2]], names=["let", "num"] ), ) df df.rename_axis(index={"let": "abc"}) df.rename_axis(index=str.upper) .. _basics.iteration: Iteration --------- The behavior of basic iteration over pandas objects depends on the type. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. DataFrames follow the dict-like convention of iterating over the "keys" of the objects. In short, basic iteration (``for i in object``) produces: * **Series**: values * **DataFrame**: column labels Thus, for example, iterating over a DataFrame gives you the column names: .. ipython:: python df = pd.DataFrame( {"col1": np.random.randn(3), "col2": np.random.randn(3)}, index=["a", "b", "c"] ) for col in df: print(col) pandas objects also have the dict-like :meth:`~DataFrame.items` method to iterate over the (key, value) pairs. To iterate over the rows of a DataFrame, you can use the following methods: * :meth:`~DataFrame.iterrows`: Iterate over the rows of a DataFrame as (index, Series) pairs. This converts the rows to Series objects, which can change the dtypes and has some performance implications. * :meth:`~DataFrame.itertuples`: Iterate over the rows of a DataFrame as namedtuples of the values. This is a lot faster than :meth:`~DataFrame.iterrows`, and is in most cases preferable to use to iterate over the values of a DataFrame. .. warning:: Iterating through pandas objects is generally **slow**. In many cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: * Look for a *vectorized* solution: many operations can be performed using built-in methods or NumPy functions, (boolean) indexing, ... * When you have a function that cannot work on the full DataFrame/Series at once, it is better to use :meth:`~DataFrame.apply` instead of iterating over the values. See the docs on :ref:`function application `. * If you need to do iterative manipulations on the values but performance is important, consider writing the inner loop with cython or numba. See the :ref:`enhancing performance ` section for some examples of this approach. .. warning:: You should **never modify** something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect! For example, in the following case setting the value has no effect: .. ipython:: python df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}) for index, row in df.iterrows(): row["a"] = 10 df items ~~~~~ Consistent with the dict-like interface, :meth:`~DataFrame.items` iterates through key-value pairs: * **Series**: (index, scalar value) pairs * **DataFrame**: (column, Series) pairs For example: .. ipython:: python for label, ser in df.items(): print(label) print(ser) .. _basics.iterrows: iterrows ~~~~~~~~ :meth:`~DataFrame.iterrows` allows you to iterate through the rows of a DataFrame as Series objects. It returns an iterator yielding each index value along with a Series containing the data in each row: .. ipython:: python for row_index, row in df.iterrows(): print(row_index, row, sep="\n") .. note:: Because :meth:`~DataFrame.iterrows` returns a Series for each row, it does **not** preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example, .. ipython:: python df_orig = pd.DataFrame([[1, 1.5]], columns=["int", "float"]) df_orig.dtypes row = next(df_orig.iterrows())[1] row All values in ``row``, returned as a Series, are now upcasted to floats, also the original integer value in column ``x``: .. ipython:: python row["int"].dtype df_orig["int"].dtype To preserve dtypes while iterating over the rows, it is better to use :meth:`~DataFrame.itertuples` which returns namedtuples of the values and which is generally much faster than :meth:`~DataFrame.iterrows`. For instance, a contrived way to transpose the DataFrame would be: .. ipython:: python df2 = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}) print(df2) print(df2.T) df2_t = pd.DataFrame({idx: values for idx, values in df2.iterrows()}) print(df2_t) itertuples ~~~~~~~~~~ The :meth:`~DataFrame.itertuples` method will return an iterator yielding a namedtuple for each row in the DataFrame. The first element of the tuple will be the row's corresponding index value, while the remaining values are the row values. For instance: .. ipython:: python for row in df.itertuples(): print(row) This method does not convert the row to a Series object; it merely returns the values inside a namedtuple. Therefore, :meth:`~DataFrame.itertuples` preserves the data type of the values and is generally faster than :meth:`~DataFrame.iterrows`. .. note:: The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. With a large number of columns (>255), regular tuples are returned. .. _basics.dt_accessors: .dt accessor ------------ ``Series`` has an accessor to succinctly return datetime like properties for the *values* of the Series, if it is a datetime/period like Series. This will return a Series, indexed like the existing Series. .. ipython:: python # datetime s = pd.Series(pd.date_range("20130101 09:10:12", periods=4)) s s.dt.hour s.dt.second s.dt.day This enables nice expressions like this: .. ipython:: python s[s.dt.day == 2] You can easily produces tz aware transformations: .. ipython:: python stz = s.dt.tz_localize("US/Eastern") stz stz.dt.tz You can also chain these types of operations: .. ipython:: python s.dt.tz_localize("UTC").dt.tz_convert("US/Eastern") You can also format datetime values as strings with :meth:`Series.dt.strftime` which supports the same format as the standard :meth:`~datetime.datetime.strftime`. .. ipython:: python # DatetimeIndex s = pd.Series(pd.date_range("20130101", periods=4)) s s.dt.strftime("%Y/%m/%d") .. ipython:: python # PeriodIndex s = pd.Series(pd.period_range("20130101", periods=4)) s s.dt.strftime("%Y/%m/%d") The ``.dt`` accessor works for period and timedelta dtypes. .. ipython:: python # period s = pd.Series(pd.period_range("20130101", periods=4, freq="D")) s s.dt.year s.dt.day .. ipython:: python # timedelta s = pd.Series(pd.timedelta_range("1 day 00:00:05", periods=4, freq="s")) s s.dt.days s.dt.seconds s.dt.components .. note:: ``Series.dt`` will raise a ``TypeError`` if you access with a non-datetime-like values. Vectorized string methods ------------------------- Series is equipped with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods exclude missing/NA values automatically. These are accessed via the Series's ``str`` attribute and generally have names matching the equivalent (scalar) built-in string methods. For example: .. ipython:: python s = pd.Series( ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="string" ) s.str.lower() Powerful pattern-matching methods are provided as well, but note that pattern-matching generally uses `regular expressions `__ by default (and in some cases always uses them). .. note:: Prior to pandas 1.0, string methods were only available on ``object`` -dtype ``Series``. pandas 1.0 added the :class:`StringDtype` which is dedicated to strings. See :ref:`text.types` for more. Please see :ref:`Vectorized String Methods ` for a complete description. .. _basics.sorting: Sorting ------- pandas supports three kinds of sorting: sorting by index labels, sorting by column values, and sorting by a combination of both. .. _basics.sort_index: By index ~~~~~~~~ The :meth:`Series.sort_index` and :meth:`DataFrame.sort_index` methods are used to sort a pandas object by its index levels. .. ipython:: python df = pd.DataFrame( { "one": pd.Series(np.random.randn(3), index=["a", "b", "c"]), "two": pd.Series(np.random.randn(4), index=["a", "b", "c", "d"]), "three": pd.Series(np.random.randn(3), index=["b", "c", "d"]), } ) unsorted_df = df.reindex( index=["a", "d", "c", "b"], columns=["three", "two", "one"] ) unsorted_df # DataFrame unsorted_df.sort_index() unsorted_df.sort_index(ascending=False) unsorted_df.sort_index(axis=1) # Series unsorted_df["three"].sort_index() .. _basics.sort_index_key: Sorting by index also supports a ``key`` parameter that takes a callable function to apply to the index being sorted. For ``MultiIndex`` objects, the key is applied per-level to the levels specified by ``level``. .. ipython:: python s1 = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3], "c": [2, 3, 4]}).set_index( list("ab") ) s1 .. ipython:: python s1.sort_index(level="a") s1.sort_index(level="a", key=lambda idx: idx.str.lower()) For information on key sorting by value, see :ref:`value sorting `. .. _basics.sort_values: By values ~~~~~~~~~ The :meth:`Series.sort_values` method is used to sort a ``Series`` by its values. The :meth:`DataFrame.sort_values` method is used to sort a ``DataFrame`` by its column or row values. The optional ``by`` parameter to :meth:`DataFrame.sort_values` may used to specify one or more columns to use to determine the sorted order. .. ipython:: python df1 = pd.DataFrame( {"one": [2, 1, 1, 1], "two": [1, 3, 2, 4], "three": [5, 4, 3, 2]} ) df1.sort_values(by="two") The ``by`` parameter can take a list of column names, e.g.: .. ipython:: python df1[["one", "two", "three"]].sort_values(by=["one", "two"]) These methods have special treatment of NA values via the ``na_position`` argument: .. ipython:: python s[2] = np.nan s.sort_values() s.sort_values(na_position="first") .. _basics.sort_value_key: Sorting also supports a ``key`` parameter that takes a callable function to apply to the values being sorted. .. ipython:: python s1 = pd.Series(["B", "a", "C"]) .. ipython:: python s1.sort_values() s1.sort_values(key=lambda x: x.str.lower()) ``key`` will be given the :class:`Series` of values and should return a ``Series`` or array of the same shape with the transformed values. For ``DataFrame`` objects, the key is applied per column, so the key should still expect a Series and return a Series, e.g. .. ipython:: python df = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3]}) .. ipython:: python df.sort_values(by="a") df.sort_values(by="a", key=lambda col: col.str.lower()) The name or type of each column can be used to apply different functions to different columns. .. _basics.sort_indexes_and_values: By indexes and values ~~~~~~~~~~~~~~~~~~~~~ Strings passed as the ``by`` parameter to :meth:`DataFrame.sort_values` may refer to either columns or index level names. .. ipython:: python # Build MultiIndex idx = pd.MultiIndex.from_tuples( [("a", 1), ("a", 2), ("a", 2), ("b", 2), ("b", 1), ("b", 1)] ) idx.names = ["first", "second"] # Build DataFrame df_multi = pd.DataFrame({"A": np.arange(6, 0, -1)}, index=idx) df_multi Sort by 'second' (index) and 'A' (column) .. ipython:: python df_multi.sort_values(by=["second", "A"]) .. note:: If a string matches both a column name and an index level name then a warning is issued and the column takes precedence. This will result in an ambiguity error in a future version. .. _basics.searchsorted: searchsorted ~~~~~~~~~~~~ Series has the :meth:`~Series.searchsorted` method, which works similarly to :meth:`numpy.ndarray.searchsorted`. .. ipython:: python ser = pd.Series([1, 2, 3]) ser.searchsorted([0, 3]) ser.searchsorted([0, 4]) ser.searchsorted([1, 3], side="right") ser.searchsorted([1, 3], side="left") ser = pd.Series([3, 1, 2]) ser.searchsorted([0, 3], sorter=np.argsort(ser)) .. _basics.nsorted: smallest / largest values ~~~~~~~~~~~~~~~~~~~~~~~~~ ``Series`` has the :meth:`~Series.nsmallest` and :meth:`~Series.nlargest` methods which return the smallest or largest :math:`n` values. For a large ``Series`` this can be much faster than sorting the entire Series and calling ``head(n)`` on the result. .. ipython:: python s = pd.Series(np.random.permutation(10)) s s.sort_values() s.nsmallest(3) s.nlargest(3) ``DataFrame`` also has the ``nlargest`` and ``nsmallest`` methods. .. ipython:: python df = pd.DataFrame( { "a": [-2, -1, 1, 10, 8, 11, -1], "b": list("abdceff"), "c": [1.0, 2.0, 4.0, 3.2, np.nan, 3.0, 4.0], } ) df.nlargest(3, "a") df.nlargest(5, ["a", "c"]) df.nsmallest(3, "a") df.nsmallest(5, ["a", "c"]) .. _basics.multiindex_sorting: Sorting by a MultiIndex column ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to ``by``. .. ipython:: python df1.columns = pd.MultiIndex.from_tuples( [("a", "one"), ("a", "two"), ("b", "three")] ) df1.sort_values(by=("a", "two")) Copying ------- The :meth:`~DataFrame.copy` method on pandas objects copies the underlying data (though not the axis indexes, since they are immutable) and returns a new object. Note that **it is seldom necessary to copy objects**. For example, there are only a handful of ways to alter a DataFrame *in-place*: * Inserting, deleting, or modifying a column. * Assigning to the ``index`` or ``columns`` attributes. * For homogeneous data, directly modifying the values via the ``values`` attribute or advanced indexing. To be clear, no pandas method has the side effect of modifying your data; almost every method returns a new object, leaving the original object untouched. If the data is modified, it is because you did so explicitly. .. _basics.dtypes: dtypes ------ For the most part, pandas uses NumPy arrays and dtypes for Series or individual columns of a DataFrame. NumPy provides support for ``float``, ``int``, ``bool``, ``timedelta64[ns]`` and ``datetime64[ns]`` (note that NumPy does not support timezone-aware datetimes). pandas and third-party libraries *extend* NumPy's type system in a few places. This section describes the extensions pandas has made internally. See :ref:`extending.extension-types` for how to write your own extension that works with pandas. See `the ecosystem page `_ for a list of third-party libraries that have implemented an extension. The following table lists all of pandas extension types. For methods requiring ``dtype`` arguments, strings can be specified as indicated. See the respective documentation sections for more on each type. +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ | Kind of Data | Data Type | Scalar | Array | String Aliases | +=================================================+===============+===========+========+===========+===============================+========================================+ | :ref:`tz-aware datetime ` | :class:`DatetimeTZDtype` | :class:`Timestamp` | :class:`arrays.DatetimeArray` | ``'datetime64[ns, ]'`` | | | | | | | +-------------------------------------------------+---------------+-----------+--------------------+-------------------------------+----------------------------------------+ | :ref:`Categorical ` | :class:`CategoricalDtype` | (none) | :class:`Categorical` | ``'category'`` | +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ | :ref:`period (time spans) ` | :class:`PeriodDtype` | :class:`Period` | :class:`arrays.PeriodArray` | ``'period[]'``, | | | | | ``'Period[]'`` | | +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ | :ref:`sparse ` | :class:`SparseDtype` | (none) | :class:`arrays.SparseArray` | ``'Sparse'``, ``'Sparse[int]'``, | | | | | | ``'Sparse[float]'`` | +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ | :ref:`intervals ` | :class:`IntervalDtype` | :class:`Interval` | :class:`arrays.IntervalArray` | ``'interval'``, ``'Interval'``, | | | | | | ``'Interval[]'``, | | | | | | ``'Interval[datetime64[ns, ]]'``, | | | | | | ``'Interval[timedelta64[]]'`` | +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ | :ref:`nullable integer ` | :class:`Int64Dtype`, ... | (none) | :class:`arrays.IntegerArray` | ``'Int8'``, ``'Int16'``, ``'Int32'``, | | | | | | ``'Int64'``, ``'UInt8'``, ``'UInt16'``,| | | | | | ``'UInt32'``, ``'UInt64'`` | +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ | :ref:`nullable float ` | :class:`Float64Dtype`, ...| (none) | :class:`arrays.FloatingArray` | ``'Float32'``, ``'Float64'`` | +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ | :ref:`Strings ` | :class:`StringDtype` | :class:`str` | :class:`arrays.StringArray` | ``'string'`` | +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ | :ref:`Boolean (with NA) ` | :class:`BooleanDtype` | :class:`bool` | :class:`arrays.BooleanArray` | ``'boolean'`` | +-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+ pandas has two ways to store strings. 1. ``object`` dtype, which can hold any Python object, including strings. 2. :class:`StringDtype`, which is dedicated to strings. Generally, we recommend using :class:`StringDtype`. See :ref:`text.types` for more. Finally, arbitrary objects may be stored using the ``object`` dtype, but should be avoided to the extent possible (for performance and interoperability with other libraries and methods. See :ref:`basics.object_conversion`). A convenient :attr:`~DataFrame.dtypes` attribute for DataFrame returns a Series with the data type of each column. .. ipython:: python dft = pd.DataFrame( { "A": np.random.rand(3), "B": 1, "C": "foo", "D": pd.Timestamp("20010102"), "E": pd.Series([1.0] * 3).astype("float32"), "F": False, "G": pd.Series([1] * 3, dtype="int8"), } ) dft dft.dtypes On a ``Series`` object, use the :attr:`~Series.dtype` attribute. .. ipython:: python dft["A"].dtype If a pandas object contains data with multiple dtypes *in a single column*, the dtype of the column will be chosen to accommodate all of the data types (``object`` is the most general). .. ipython:: python # these ints are coerced to floats pd.Series([1, 2, 3, 4, 5, 6.0]) # string data forces an ``object`` dtype pd.Series([1, 2, 3, 6.0, "foo"]) The number of columns of each type in a ``DataFrame`` can be found by calling ``DataFrame.dtypes.value_counts()``. .. ipython:: python dft.dtypes.value_counts() Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the ``dtype`` keyword, a passed ``ndarray``, or a passed ``Series``), then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste. .. ipython:: python df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float32") df1 df1.dtypes df2 = pd.DataFrame( { "A": pd.Series(np.random.randn(8), dtype="float16"), "B": pd.Series(np.random.randn(8)), "C": pd.Series(np.random.randint(0, 255, size=8), dtype="uint8"), # [0,255] (range of uint8) } ) df2 df2.dtypes defaults ~~~~~~~~ By default integer types are ``int64`` and float types are ``float64``, *regardless* of platform (32-bit or 64-bit). The following will all result in ``int64`` dtypes. .. ipython:: python pd.DataFrame([1, 2], columns=["a"]).dtypes pd.DataFrame({"a": [1, 2]}).dtypes pd.DataFrame({"a": 1}, index=list(range(2))).dtypes Note that Numpy will choose *platform-dependent* types when creating arrays. The following **WILL** result in ``int32`` on 32-bit platform. .. ipython:: python frame = pd.DataFrame(np.array([1, 2])) upcasting ~~~~~~~~~ Types can potentially be *upcasted* when combined with other types, meaning they are promoted from the current type (e.g. ``int`` to ``float``). .. ipython:: python df3 = df1.reindex_like(df2).fillna(value=0.0) + df2 df3 df3.dtypes :meth:`DataFrame.to_numpy` will return the *lower-common-denominator* of the dtypes, meaning the dtype that can accommodate **ALL** of the types in the resulting homogeneous dtyped NumPy array. This can force some *upcasting*. .. ipython:: python df3.to_numpy().dtype astype ~~~~~~ .. _basics.cast: You can use the :meth:`~DataFrame.astype` method to explicitly convert dtypes from one to another. These will by default return a copy, even if the dtype was unchanged (pass ``copy=False`` to change this behavior). In addition, they will raise an exception if the astype operation is invalid. Upcasting is always according to the **NumPy** rules. If two different dtypes are involved in an operation, then the more *general* one will be used as the result of the operation. .. ipython:: python df3 df3.dtypes # conversion of dtypes df3.astype("float32").dtypes Convert a subset of columns to a specified type using :meth:`~DataFrame.astype`. .. ipython:: python dft = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}) dft[["a", "b"]] = dft[["a", "b"]].astype(np.uint8) dft dft.dtypes Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFrame.astype`. .. ipython:: python dft1 = pd.DataFrame({"a": [1, 0, 1], "b": [4, 5, 6], "c": [7, 8, 9]}) dft1 = dft1.astype({"a": np.bool_, "c": np.float64}) dft1 dft1.dtypes .. note:: When trying to convert a subset of columns to a specified type using :meth:`~DataFrame.astype` and :meth:`~DataFrame.loc`, upcasting occurs. :meth:`~DataFrame.loc` tries to fit in what we are assigning to the current dtypes, while ``[]`` will overwrite them taking the dtype from the right hand side. Therefore the following piece of code produces the unintended result. .. ipython:: python dft = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}) dft.loc[:, ["a", "b"]].astype(np.uint8).dtypes dft.loc[:, ["a", "b"]] = dft.loc[:, ["a", "b"]].astype(np.uint8) dft.dtypes .. _basics.object_conversion: object conversion ~~~~~~~~~~~~~~~~~ pandas offers various functions to try to force conversion of types from the ``object`` dtype to other types. In cases where the data is already of the correct type, but stored in an ``object`` array, the :meth:`DataFrame.infer_objects` and :meth:`Series.infer_objects` methods can be used to soft convert to the correct type. .. ipython:: python import datetime df = pd.DataFrame( [ [1, 2], ["a", "b"], [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)], ] ) df = df.T df df.dtypes Because the data was transposed the original inference stored all columns as object, which ``infer_objects`` will correct. .. ipython:: python df.infer_objects().dtypes The following functions are available for one dimensional object arrays or scalars to perform hard conversion of objects to a specified type: * :meth:`~pandas.to_numeric` (conversion to numeric dtypes) .. ipython:: python m = ["1.1", 2, 3] pd.to_numeric(m) * :meth:`~pandas.to_datetime` (conversion to datetime objects) .. ipython:: python import datetime m = ["2016-07-09", datetime.datetime(2016, 3, 2)] pd.to_datetime(m) * :meth:`~pandas.to_timedelta` (conversion to timedelta objects) .. ipython:: python m = ["5us", pd.Timedelta("1day")] pd.to_timedelta(m) To force a conversion, we can pass in an ``errors`` argument, which specifies how pandas should deal with elements that cannot be converted to desired dtype or object. By default, ``errors='raise'``, meaning that any errors encountered will be raised during the conversion process. However, if ``errors='coerce'``, these errors will be ignored and pandas will convert problematic elements to ``pd.NaT`` (for datetime and timedelta) or ``np.nan`` (for numeric). This might be useful if you are reading in data which is mostly of the desired dtype (e.g. numeric, datetime), but occasionally has non-conforming elements intermixed that you want to represent as missing: .. ipython:: python :okwarning: import datetime m = ["apple", datetime.datetime(2016, 3, 2)] pd.to_datetime(m, errors="coerce") m = ["apple", 2, 3] pd.to_numeric(m, errors="coerce") m = ["apple", pd.Timedelta("1day")] pd.to_timedelta(m, errors="coerce") In addition to object conversion, :meth:`~pandas.to_numeric` provides another argument ``downcast``, which gives the option of downcasting the newly (or already) numeric data to a smaller dtype, which can conserve memory: .. ipython:: python m = ["1", 2, 3] pd.to_numeric(m, downcast="integer") # smallest signed int dtype pd.to_numeric(m, downcast="signed") # same as 'integer' pd.to_numeric(m, downcast="unsigned") # smallest unsigned int dtype pd.to_numeric(m, downcast="float") # smallest float dtype As these methods apply only to one-dimensional arrays, lists or scalars; they cannot be used directly on multi-dimensional objects such as DataFrames. However, with :meth:`~pandas.DataFrame.apply`, we can "apply" the function over each column efficiently: .. ipython:: python import datetime df = pd.DataFrame([["2016-07-09", datetime.datetime(2016, 3, 2)]] * 2, dtype="O") df df.apply(pd.to_datetime) df = pd.DataFrame([["1.1", 2, 3]] * 2, dtype="O") df df.apply(pd.to_numeric) df = pd.DataFrame([["5us", pd.Timedelta("1day")]] * 2, dtype="O") df df.apply(pd.to_timedelta) gotchas ~~~~~~~ Performing selection operations on ``integer`` type data can easily upcast the data to ``floating``. The dtype of the input data will be preserved in cases where ``nans`` are not introduced. See also :ref:`Support for integer NA `. .. ipython:: python dfi = df3.astype("int32") dfi["E"] = 1 dfi dfi.dtypes casted = dfi[dfi > 0] casted casted.dtypes While float dtypes are unchanged. .. ipython:: python dfa = df3.copy() dfa["A"] = dfa["A"].astype("float32") dfa.dtypes casted = dfa[df2 > 0] casted casted.dtypes Selecting columns based on ``dtype`` ------------------------------------ .. _basics.selectdtypes: The :meth:`~DataFrame.select_dtypes` method implements subsetting of columns based on their ``dtype``. First, let's create a :class:`DataFrame` with a slew of different dtypes: .. ipython:: python df = pd.DataFrame( { "string": list("abc"), "int64": list(range(1, 4)), "uint8": np.arange(3, 6).astype("u1"), "float64": np.arange(4.0, 7.0), "bool1": [True, False, True], "bool2": [False, True, False], "dates": pd.date_range("now", periods=3), "category": pd.Series(list("ABC")).astype("category"), } ) df["tdeltas"] = df.dates.diff() df["uint64"] = np.arange(3, 6).astype("u8") df["other_dates"] = pd.date_range("20130101", periods=3) df["tz_aware_dates"] = pd.date_range("20130101", periods=3, tz="US/Eastern") df And the dtypes: .. ipython:: python df.dtypes :meth:`~DataFrame.select_dtypes` has two parameters ``include`` and ``exclude`` that allow you to say "give me the columns *with* these dtypes" (``include``) and/or "give the columns *without* these dtypes" (``exclude``). For example, to select ``bool`` columns: .. ipython:: python df.select_dtypes(include=[bool]) You can also pass the name of a dtype in the `NumPy dtype hierarchy `__: .. ipython:: python df.select_dtypes(include=["bool"]) :meth:`~pandas.DataFrame.select_dtypes` also works with generic dtypes as well. For example, to select all numeric and boolean columns while excluding unsigned integers: .. ipython:: python df.select_dtypes(include=["number", "bool"], exclude=["unsignedinteger"]) To select string columns you must use the ``object`` dtype: .. ipython:: python df.select_dtypes(include=["object"]) To see all the child dtypes of a generic ``dtype`` like ``numpy.number`` you can define a function that returns a tree of child dtypes: .. ipython:: python def subdtypes(dtype): subs = dtype.__subclasses__() if not subs: return dtype return [dtype, [subdtypes(dt) for dt in subs]] All NumPy dtypes are subclasses of ``numpy.generic``: .. ipython:: python subdtypes(np.generic) .. note:: pandas also defines the types ``category``, and ``datetime64[ns, tz]``, which are not integrated into the normal NumPy hierarchy and won't show up with the above function.