Most of the rest of the book is working with data once it's in a pandas data structure, but this recipe is about those structures themselves and getting data in and out of them. Open your interpreter, preferably IPython.

• Create an incredibly simple DataFrame to start with. A DataFrame can handle lists, NumPy arrays, dicts of strings, and more.
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• The first example is too simple, and isn't useful. Add some column headers and index for more information about the DataFrame.
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Downloading the example code

You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

Most of the file input and output in pandas is the orchestration behind the scenes of formatting the value outputs, and then writing those values to a file. There are many options for formatting file output. The to_csv method takes many parameters. Some of the more common parameters are as follows:

• sep: It specifies the value to separate with, in the output file
• index: It is a Boolean that decides whether or not to print the index
• na_rep: It specifies what to substitute for the na values
  

The following snippet writes the DataFrame df and writes it to a file called file.tsv, and it's formatted according to the parameters passed to the method.

In addition to standard file input and output functionalities, pandas has several built-in niceties.

Using Panda's sophisticated date parser, a CSV can read and parse dates at the same time, as shown in the following command line:

Besides the parsing capabilities, pandas also has a very handy date_range function, which returns a range of dates determined by the inputs. For example, it's very easy to get the months of 2012 in a series. This is shown in the following command line:

pandas can also read CSV data from the Web, assuming http://www.example.com/data.csv is the URL. Take a look at the following example:

Open up your interpreter, and execute the following steps:

• Create a simple DataFrame to explore the different slicing abilities of pandas.
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• Select the first two rows of the column named 'one'.
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• Pass an array of column names instead of 'one'.
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• Use a negative index to navigate backwards through the DataFrame.
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• Select every fifth row from the DataFrame df.
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• Use the head and tail functions to easily select the top and bottom of the DataFrame.
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At some level, pandas objects behave similar to NumPy arrays; they are after all abstractions built on top of them. However, because we have more metadata about the data structures we can use that to our advantage.

After the initial pandas object is created, simple slicing occurs according to the following structure:

Here column names is a string (or an array, if multiple columns) and rows is the number of rows that we wish to use.

The methods that have already been described are very useful at a higher level, but there are more granular operations available.

The .ix command is an advanced method for selecting and slicing a DataFrame. Taking the sample from the preceding example, df.ix[1:3 ,[ 'one', 'two']] = 10will not only select the specified subset of the data, but also set its value equal to 10. The .xs command has a more explicit interface for working with indexes.

Often, the index of the DataFrame becomes out of alignment when slicing data. In pandas, the easiest way to reset an index is with the reset_index() method of the DataFrame object.

Open up your interpreter and follow the command progression in the following section. Difficult financial analysis was the mother of pandas creation; therefore, it has many efficient and easy ways for dealing with dates.

• Let's examine the date_range functionality within pandas.
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• Create a time series and slice it by passing a range of dates to Series.
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The date_range function is defined by dates and frequencies. See the following section for the various frequency designations. The easiest way is to define a start date, end date, and frequency, but there are other ways as well. You can also change the frequency, or resample to a smaller or larger time interval.

pandas adds a lot more functionalities to handle dates. These are mostly convenient methods because working with dates is a necessary evil of data analysis.

Time series in pandas don't have to be defined by a start and end date. In pandas, it is possible to represent the time of the Series as an interval of dates with a common period between data points. For example, if we want to create a Series just like Y2K, we can do so as follows:

pandas offers the ability to move up and down the granularity of a time series. For example, given a Series of random numbers s for all the days in 2012, calculating the sum for each month is done by the following formula:

In the preceding example, the 'M' variable specifies that we're upsampling to month. Downsampling is also done in a similar way; however, pandas provides functionalities for handling the disaggregation in a convenient way.