How To Download Numpy And Pandas On Mac

In this Python Programming video tutorial you will learn about how to install numpy scipy matplotlib and pandas libraries in detail. NumPy is a library for t. How do you install pandas and numpy in Mac computer and Mac terminal command? Previous question Next question Get more help from Chegg. You'll get numpy, scipy, pandas (and a set of other very useful scientific packages) installed on your system and automatically added to your PATH. Download the installer and follow the instructions on the install page. After the install is complete, open the command prompt and type jupyter notebook on.

The easiest way to install pandas is to install itas part of the Anaconda distribution, across platform distribution for data analysis and scientific computing.This is the recommended installation method for most users.

Installing with Anaconda¶. Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way to install not only pandas, but Python and the most popular packages that make up the SciPy stack (IPython, NumPy, Matplotlib, ) is with Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics.

Instructions for installing from source,PyPI, ActivePython, various Linux distributions, or adevelopment version are also provided.

Python version support¶

Officially Python 3.6.1 and above, 3.7, 3.8, and 3.9.

Installing pandas¶

Installing with Anaconda¶

Installing pandas and the rest of the NumPy andSciPy stack can be a littledifficult for inexperienced users.

The simplest way to install not only pandas, but Python and the most popularpackages that make up the SciPy stack(IPython, NumPy,Matplotlib, …) is withAnaconda, a cross-platform(Linux, Mac OS X, Windows) Python distribution for data analytics andscientific computing.

After running the installer, the user will have access to pandas and therest of the SciPy stack without needing to installanything else, and without needing to wait for any software to be compiled.

Installation instructions for Anacondacan be found here.

A full list of the packages available as part of theAnaconda distributioncan be found here.

Another advantage to installing Anaconda is that you don’t needadmin rights to install it. Anaconda can install in the user’s home directory,which makes it trivial to delete Anaconda if you decide (just deletethat folder).

Installing with Miniconda¶

The previous section outlined how to get pandas installed as part of theAnaconda distribution.However this approach means you will install well over one hundred packagesand involves downloading the installer which is a few hundred megabytes in size.

If you want to have more control on which packages, or have a limited internetbandwidth, then installing pandas withMiniconda may be a better solution.

Conda is the package manager that theAnaconda distribution is built upon.It is a package manager that is both cross-platform and language agnostic(it can play a similar role to a pip and virtualenv combination).

Miniconda allows you to create aminimal self contained Python installation, and then use theConda command to install additional packages.

First you will need Conda to be installed anddownloading and running the Minicondawill do this for you. The installercan be found here

How To Download Numpy And Pandas On Mac

The next step is to create a new conda environment. A conda environment is like avirtualenv that allows you to specify a specific version of Python and set of libraries.Run the following commands from a terminal window:

This will create a minimal environment with only Python installed in it.To put your self inside this environment run:

On Windows the command is:

The final step required is to install pandas. This can be done with thefollowing command:

To install a specific pandas version:

To install other packages, IPython for example:

To install the full Anacondadistribution:

If you need packages that are available to pip but not conda, theninstall pip, and then use pip to install those packages:

Installing from PyPI¶

pandas can be installed via pip fromPyPI.

Installing with ActivePython¶

How To Download Numpy And Pandas On Mac 10

Installation instructions forActivePython can be foundhere. Versions2.7, 3.5 and 3.6 include pandas.

Installing using your Linux distribution’s package manager.¶

The commands in this table will install pandas for Python 3 from your distribution.

Distribution

Status

Download / Repository Link

Install method

Debian

stable

sudoapt-getinstallpython3-pandas

Debian & Ubuntu

unstable (latest packages)

sudoapt-getinstallpython3-pandas

Ubuntu

stable

sudoapt-getinstallpython3-pandas

OpenSuse

stable

zypperinpython3-pandas

Fedora

stable

dnfinstallpython3-pandas

Centos/RHEL

stable

yuminstallpython3-pandas

However, the packages in the linux package managers are often a few versions behind, soto get the newest version of pandas, it’s recommended to install using the pip or condamethods described above.

Handling ImportErrors¶

If you encounter an ImportError, it usually means that Python couldn’t find pandas in the list of availablelibraries. Python internally has a list of directories it searches through, to find packages. You canobtain these directories with:

One way you could be encountering this error is if you have multiple Python installations on your systemand you don’t have pandas installed in the Python installation you’re currently using.In Linux/Mac you can run whichpython on your terminal and it will tell you which Python installation you’reusing. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended.

It is highly recommended to use conda, for quick installation and for package and dependency updates.You can find simple installation instructions for pandas in this document: installation instructions </getting_started.html>.

Installing from source¶

How To Download Numpy And Pandas On Mac Download

See the contributing guide for complete instructions on building from the git source tree. Further, see creating a development environment if you wish to create a pandas development environment.

Running the test suite¶

pandas is equipped with an exhaustive set of unit tests, covering about 97% ofthe code base as of this writing. To run it on your machine to verify thateverything is working (and that you have all of the dependencies, soft and hard,installed), make sure you have pytest >= 5.0.1 and Hypothesis >= 3.58, then run:

Dependencies¶

Package

Minimum supported version

24.2.0

1.15.4

2.7.3

2017.2

Recommended dependencies¶

  • numexpr: for accelerating certain numerical operations.numexpr uses multiple cores as well as smart chunking and caching to achieve large speedups.If installed, must be Version 2.6.2 or higher.

  • bottleneck: for accelerating certain types of nanevaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed,must be Version 1.2.1 or higher.

Note

You are highly encouraged to install these libraries, as they provide speed improvements, especiallywhen working with large data sets.

Optional dependencies¶

Pandas has many optional dependencies that are only used for specific methods.For example, pandas.read_hdf() requires the pytables package, whileDataFrame.to_markdown() requires the tabulate package. If theoptional dependency is not installed, pandas will raise an ImportError whenthe method requiring that dependency is called.

Dependency

Minimum Version

Notes

BeautifulSoup4

4.6.0

HTML parser for read_html (see note)

Jinja2

Conditional formatting with DataFrame.style

PyQt4

Clipboard I/O

PyQt5

Clipboard I/O

PyTables

3.4.3

HDF5-based reading / writing

SQLAlchemy

1.1.4

SQL support for databases other than sqlite

SciPy

0.19.0

Miscellaneous statistical functions

XLsxWriter

0.9.8

Excel writing

blosc

Compression for HDF5

fsspec

0.7.4

Handling files aside from local and HTTP

fastparquet

0.3.2

Parquet reading / writing

gcsfs

0.6.0

Google Cloud Storage access

html5lib

HTML parser for read_html (see note)

lxml

3.8.0

HTML parser for read_html (see note)

matplotlib

2.2.2

Visualization

numba

0.46.0

Alternative execution engine for rolling operations

openpyxl

2.5.7

Reading / writing for xlsx files

pandas-gbq

0.12.0

Google Big Query access

psycopg2

PostgreSQL engine for sqlalchemy

pyarrow

0.12.0

Parquet, ORC (requires 0.13.0), and feather reading / writing

pymysql

0.7.11

MySQL engine for sqlalchemy

pyreadstat

SPSS files (.sav) reading

pytables

3.4.3

HDF5 reading / writing

pyxlsb

1.0.6

Reading for xlsb files

qtpy

Clipboard I/O

s3fs

0.4.0

Amazon S3 access

tabulate

0.8.3

Printing in Markdown-friendly format (see tabulate)

xarray

0.8.2

pandas-like API for N-dimensional data

xclip

Clipboard I/O on linux

xlrd

1.1.0

Excel reading

xlwt

1.2.0

Excel writing

xsel

Clipboard I/O on linux

zlib

Compression for HDF5

Optional dependencies for parsing HTML¶

One of the following combinations of libraries is needed to use thetop-level read_html() function:

  • BeautifulSoup4 and html5lib

  • BeautifulSoup4 and lxml

  • BeautifulSoup4 and html5lib and lxml

  • Only lxml, although see HTML Table Parsingfor reasons as to why you should probably not take this approach.

Warning

  • if you install BeautifulSoup4 you must install eitherlxml or html5lib or both.read_html() will not work with onlyBeautifulSoup4 installed.

  • You are highly encouraged to read HTML Table Parsing gotchas.It explains issues surrounding the installation andusage of the above three libraries.