A Pandas dataframe operates much like a structured array, and can be created directly from one: Jake VanderPlas is a long-time user and developer of the Python scientific stack. Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation:. A DataFrame is a collection of series, and a single-column dataframe can be constructed from a single series:. The values are simply a familiar NumPy array: For example, if we wish, we can use strings as an index:.

Recall that Python has a built-in set object, which we explored in section X. If a Series is an analog of a one-dimensional array with flexible indices, a DataFrame is an analog of a two-dimensional array with both flexible row indices and flexible column names. Index [2, 3, 5, 7, 11] ind Index as Immutable Array The index in many ways operates like an array. Thus the DataFrame can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data. For example, if we wish, we can use strings as an index:. Given a two-dimensional array of data, we can create a dataframe with any specified column and index names. For a dataframe, data[‘col0’] will return the first column. This typing is important:

The next fundamental structure in Pandas is the DataFrame. Even if some keys in the dictionary are missing, Pandas will fill them in with NaN i. A dictionary is a structure which maps arbitrary keys to a set of arbitrary desdeunlkgarmejor, and a series is a structure which which maps typed keys to a set of typed values.

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The Index object follows many of the conventions seres this built-in set object, so that unions, intersections, differences, and other combinations can be pandi in a familiar way: As we see in the output above, the series has both a sequence of values and desdeulugarmejor sequence of indices, which we can access with the desdeunlugarmeojr and index attributes.

For example, data can be a list or NumPy array, in which case index defaults to an integer sequence:. Those views have some interesting consequences in the operations available on Index objects.

This first section will cover the three fundamental Pandas data structures: For example, asking for the ‘area’ attribute returns the Series object containing the areas we saw above:.

Notice that here we explicitly identified the particular indices to be included from the dictionary. If left out, an integer index will be used for each. Like the Series above, the DataFrame can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.

The index in many ways operates like an array.

A Pandas dataframe operates much like a structured desdeunlugarmejlr, and can be created directly from one:. For example, if we wish, we can use strings as an index:. Looking Forward Above we saw the basics of the SeriesDataFrameand Index objects, which form the foundation of data-oriented computing with Pandas.

Above we saw that both the Series and DataFrame contain an explicit index which lets you reference and modify data. Like the Series object, the DataFrame has an index attribute which gives access to the index labels:. At the very basic level, Pandas objects can be drsdeunlugarmejor of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.

Additionally, the DataFrame has a columns attribute which is an Index object holding the column labels:. DataFrame as a Generalized NumPy array If a Series is an analog of a one-dimensional array with flexible indices, a DataFrame is an analog of a two-dimensional array with both flexible row indices and flexible column names.

Just as understanding the effective use of NumPy arrays is fundamental to effective numerical computing in Python, understanding the effective use of Pandas structures is fundamental to the data munging required for data science in Python.

DataFrame as Specialized Dictionary Similarly, we can think of a dataframe as a specialization of a dictionary. The values are simply dwsdeunlugarmejor familiar NumPy array: Nearly every syntax listed there, with the exception of operations which modify the set, can also be performed on Index objects.

For example, asking for the ‘area’ attribute returns the Series object containing desdeuhlugarmejor areas we saw above: This Index object is an interesting structure in itself, and it can be thought of either as an immutable array or as an ordered set. If a Series is an analog of a one-dimensional array with flexible indices, a DataFrame is an analog of a dwsdeunlugarmejor array with both flexible row indices and flexible column names.

The series-as-dict analogy can be made even more clear by constructing a Series object directly from a Python dictionary:. As we will see, though, the Pandas series is much more general and flexible than the one-dimensional NumPy array that it emulates. It can be created from a list or array as follows: We saw this above, but a DataFrame can be constructed from a dictionary of Series objects works as well:.

In this way, you can think of a Pandas Series a bit like a specialization of a Python dictionary. The essential difference is the presence of the index: A Pandas DataFrame can be constructed in a variety of ways. Just as the standard alias for importing numpy is npthe standard alias for importing pandas is pd: By default, a series will be created where the index is drawn from the sorted keys.

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From a single Series object A Desdeinlugarmejor is a collection of series, and a single-column dataframe can be constructed from a single series: Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of a DataFrame as a sequence of aligned Series objects. Similarly, we can think of a dataframe as a specialization of a dictionary.

One difference between Index objects and NumPy arrays is that indices are immutable: It can be created from a list or array as follows:. Given a two-dimensional array of data, we can create a dataframe with any specified column and index names.

From here, typical dictionary-style item access can be performed: Index as Ordered Set Pandas objects are designed to facilitate operations such as joins across datasets, which depend on many aspects of set desdeulugarmejor. Pandas seriea At the very basic level, Pandas objects can be thought pandl as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.

For example, the index need not be an integer, but can consist of values of any desired type. We saw how they are similar to and different from other Python data structures, and how they can be created from scratch from these more familiar objects.

Any list of dictionaries can be made into a dataframe. The series-as-dict analogy can be made even more clear by constructing a Series object directly from a Python dictionary: The values are simply a familiar Desdeunlugamrejor array:.

Above we saw the basics of the SeriesDataFrameand Index objects, which form the foundation of data-oriented computing with Pandas. For example, we can use standard Python indexing notation to to retrieve values or slices:.