Pandas Github

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1. Pandas Challenge Github
2. Pandas Dataframe Python Github
3. Pandas Github To_sql

1. Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Getting started New to pandas?
2. A REST API based on Flask for serving Pandas Dataframes to Grafana. This way, a native Python application can be used to directly supply data to Grafana both easily and powerfully. It was inspired by and is compatible with the simple json datasource.

Since Python 3.4, pathlib has been included in the Python standard library. Path objects provide a simple and delightful way to interact with the file system. The pandas-path package enables the Path API for pandas through a custom accessor.path.Getting just the filenames from a series of full file paths is as simple as myfiles.path.name.

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.).Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in Computation on NumPy Arrays: Universal Functions are key to this.

Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will preserve index and column labels in the output, and for binary operations such as addition and multiplication, Pandas will automatically align indices when passing the objects to the ufunc.This means that keeping the context of data and combining data from different sources–both potentially error-prone tasks with raw NumPy arrays–become essentially foolproof ones with Pandas.We will additionally see that there are well-defined operations between one-dimensional Series structures and two-dimensional DataFrame structures.

Ufuncs: Index Preservation¶

Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas Series and DataFrame objects.Let's start by defining a simple Series and DataFrame on which to demonstrate this:

If we apply a NumPy ufunc on either of these objects, the result will be another Pandas object with the indices preserved:

Any of the ufuncs discussed in Computation on NumPy Arrays: Universal Functions can be used in a similar manner.

UFuncs: Index Alignment¶

For binary operations on two Series or DataFrame objects, Pandas will align indices in the process of performing the operation.This is very convenient when working with incomplete data, as we'll see in some of the examples that follow.

Index alignment in Series¶

As an example, suppose we are combining two different data sources, and find only the top three US states by area and the top three US states by population:

Let's see what happens when we divide these to compute the population density:

The resulting array contains the union of indices of the two input arrays, which could be determined using standard Python set arithmetic on these indices:

Any item for which one or the other does not have an entry is marked with NaN, or 'Not a Number,' which is how Pandas marks missing data (see further discussion of missing data in Handling Missing Data).This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are filled in with NaN by default:

If using NaN values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators.For example, calling A.add(B) is equivalent to calling A + B, but allows optional explicit specification of the fill value for any elements in A or B that might be missing:

Index alignment in DataFrame¶

A similar type of alignment takes place for both columns and indices when performing operations on DataFrames:

Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted.As was the case with Series, we can use the associated object's arithmetic method and pass any desired fill_value to be used in place of missing entries.Here we'll fill with the mean of all values in A (computed by first stacking the rows of A):

The following table lists Python operators and their equivalent Pandas object methods:

Ufuncs: Operations Between DataFrame and Series¶

When performing operations between a DataFrame and a Series, the index and column alignment is similarly maintained.Operations between a DataFrame and a Series are similar to operations between a two-dimensional and one-dimensional NumPy array.Consider one common operation, where we find the difference of a two-dimensional array and one of its rows:

According to NumPy's broadcasting rules (see Computation on Arrays: Broadcasting), subtraction between a two-dimensional array and one of its rows is applied row-wise.

In Pandas, the convention similarly operates row-wise by default:

If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the axis keyword:

Note that these DataFrame/Series operations, like the operations discussed above, will automatically align indices between the two elements:

This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the types of silly errors that might come up when working with heterogeneous and/or misaligned data in raw NumPy arrays.

< Data Indexing and Selection | Contents | Handling Missing Data >

Up to date remote data access for pandas, works for multiple versions of pandas.

Warning

As of v0.7.0 Google finance and Morningstar have been been immediately deprecated due tolarge changes in their API and no stable replacement.

Usage¶

Starting in 0.19.0, pandas no longer supports pandas.io.data or pandas.io.wb, soyou must replace your imports from pandas.io with those from pandas_datareader:

Many functions from the data module have been included in the top level API.

Documentation¶

Stable documentationis available ongithub.io.A second copy of the stable documentation is hosted onread the docs for more details.

As an example, suppose we are combining two different data sources, and find only the top three US states by area and the top three US states by population:

Let's see what happens when we divide these to compute the population density:

The resulting array contains the union of indices of the two input arrays, which could be determined using standard Python set arithmetic on these indices:

Any item for which one or the other does not have an entry is marked with NaN, or 'Not a Number,' which is how Pandas marks missing data (see further discussion of missing data in Handling Missing Data).This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are filled in with NaN by default:

If using NaN values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators.For example, calling A.add(B) is equivalent to calling A + B, but allows optional explicit specification of the fill value for any elements in A or B that might be missing:

Index alignment in DataFrame¶

A similar type of alignment takes place for both columns and indices when performing operations on DataFrames:

Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted.As was the case with Series, we can use the associated object's arithmetic method and pass any desired fill_value to be used in place of missing entries.Here we'll fill with the mean of all values in A (computed by first stacking the rows of A):

The following table lists Python operators and their equivalent Pandas object methods:

Ufuncs: Operations Between DataFrame and Series¶

When performing operations between a DataFrame and a Series, the index and column alignment is similarly maintained.Operations between a DataFrame and a Series are similar to operations between a two-dimensional and one-dimensional NumPy array.Consider one common operation, where we find the difference of a two-dimensional array and one of its rows:

According to NumPy's broadcasting rules (see Computation on Arrays: Broadcasting), subtraction between a two-dimensional array and one of its rows is applied row-wise.

In Pandas, the convention similarly operates row-wise by default:

If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the axis keyword:

Note that these DataFrame/Series operations, like the operations discussed above, will automatically align indices between the two elements:

This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the types of silly errors that might come up when working with heterogeneous and/or misaligned data in raw NumPy arrays.

< Data Indexing and Selection | Contents | Handling Missing Data >

Up to date remote data access for pandas, works for multiple versions of pandas.

Warning

As of v0.7.0 Google finance and Morningstar have been been immediately deprecated due tolarge changes in their API and no stable replacement.

Usage¶

Starting in 0.19.0, pandas no longer supports pandas.io.data or pandas.io.wb, soyou must replace your imports from pandas.io with those from pandas_datareader:

Many functions from the data module have been included in the top level API.

Documentation¶

Stable documentationis available ongithub.io.A second copy of the stable documentation is hosted onread the docs for more details.

Development documentationis available for the latest changes in master.

Installation¶

Requirements¶

Using pandas datareader requires the following packages:

Pandas Challenge Github

• pandas>=0.19.2
• lxml
• requests>=2.3.0
• wrapt

Building the documentation additionally requires:

• matplotlib
• ipython
• sphinx
• sphinx_rtd_theme

Testing requires pytest.

Install latest development version¶

or

Documentation¶

Pandas Dataframe Python Github

Contents:

Pandas Github To_sql

• What's New
• Remote Data Access
• Other Data Sources
• Data Readers