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‎MANIFEST.in

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include requirements.txt
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include requirements-dev.txt
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include pandas_datareader/*.py
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include pandas_datareader/tests/*.py
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include pandas_datareader/tests/data/*
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include versioneer.py
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include pandas_datareader/_version.py

‎README.md

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Using pandas datareader requires the following packages:
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- pandas>=1.0
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- pandas>=1.5.3
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- lxml
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- requests>=2.19.0
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‎azure-pipelines.yml

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variables:
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PYTHONHASHSEED: 12345678
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SETUPTOOLS_USE_DISTUTILS: "stdlib"
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coverage: true
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RANDOMGEN_CYTHON_COVERAGE: true
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jobs:
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- template: ci/azure/azure_template_posix.yml
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parameters:
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name: Linux
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vmImage: ubuntu-20.04
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vmImage: ubuntu-latest
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- template: ci/azure/azure_template_windows.yml
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parameters:
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name: Windows
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vmImage: windows-2019
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vmImage: windows-latest

‎ci/azure/azure_template_posix.yml

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vmImage: ${{ parameters.vmImage }}
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strategy:
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matrix:
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${{ if eq(parameters.name, 'Linux') }}:
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python36_legacy:
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python.version: '3.6'
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PANDAS: 1.0.5
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python37_legacy:
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python.version: '3.7'
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PANDAS: 1.1.5
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python38_recent:
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python.version: '3.8'
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PANDAS: 1.2.5
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python39_latest:
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python.version: '3.9'
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# maxParallel: 10
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python38_legacy:
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python.version: '3.8'
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PANDAS: 1.5.3
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python39_legacy:
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python.version: '3.9'
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PANDAS: 1.5.3
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python310_recent:
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python.version: '3.9'
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PANDAS: 2.0.3
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python311_latest:
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python.version: '3.11'
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python312_latest:
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python.version: '3.12'
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steps:
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- task: UsePythonVersion@0
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python -m pip install pip setuptools -U
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python -m pip install -r requirements.txt
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python -m pip install -r requirements-dev.txt
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if [[ -n ${PANDAS} ]]; then
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python -m pip install pandas==${PANDAS}
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fi;
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displayName: 'Install dependencies'
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- script: |
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python -m pip install -e . -v --no-build-isolation
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python -m pip install -e . -vv
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displayName: 'Install'
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- script: python -m pip list

‎ci/azure/azure_template_windows.yml

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vmImage: ${{ parameters.vmImage }}
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strategy:
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matrix:
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${{ if eq(parameters.name, 'Linux') }}:
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python37_legacy:
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python.version: '3.7'
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PANDAS: 1.0.5
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python38_legacy:
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python.version: '3.8'
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PANDAS: 1.1.5
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python38_recent:
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python.version: '3.8'
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PANDAS: 1.2.5
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python39_latest:
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python.version: '3.9'
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# maxParallel: 10
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python38_legacy:
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python.version: '3.8'
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PANDAS: 1.5.3
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python39_legecy:
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python.version: '3.9'
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PANDAS: 1.5.3
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python310_recent:
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python.version: '3.10'
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PANDAS: 2.0.3
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python311_latest:
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python.version: '3.11'
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python312_latest:
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python.version: '3.12'
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steps:
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- task: UsePythonVersion@0
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python -m pip install -r requirements.txt
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python -m pip install -r requirements-dev.txt
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if [[ -n ${PANDAS} ]]; then
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python -m pip install pandas==${PANDAS}
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fi;
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displayName: 'Install dependencies'
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- powershell: |
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if ($null -ne $env:PANDAS) {
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python -m pip install pandas==${PANDAS}
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}
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displayName: 'Update pandas (if needed)'
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- script: |
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python -m pip install -e . -v --no-build-isolation
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python -m pip install -e . -vv
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displayName: 'Install'
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- script: python -m pip list

‎ci/pypi-install.sh

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This file was deleted.

‎docs/source/conf.py

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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#
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# pandas-datareader documentation build configuration file, created by
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# sphinx-quickstart on Thu Jan 18 13:34:14 2018.
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#
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# This is also used if you do content translation via gettext catalogs.
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# Usually you set "language" from the command line for these cases.
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language = None
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language = "en"
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# Example configuration for intersphinx: refer to the Python standard library.
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intersphinx_mapping = {"https://docs.python.org/": None}
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intersphinx_mapping = {"python": ("https://docs.python.org/3", None)}
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extlinks = {

‎docs/source/remote_data.rst

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.. code-block:: ipython
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import os
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import pandas_datareader.data as web
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In [1]: import os
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f = web.DataReader('ticker=RGDPUS', 'econdb')
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f.head()
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In [2]: import pandas_datareader as pdr
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In [3]: f = pdr.get_data_econdb('ticker=RGDPUS')
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In [4]: f.head()
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Out[4]:
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TableName T10106
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SeriesCode A191RX
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Table Table 1.1.6. Real Gross Domestic Product, Ch...
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Series description Gross domestic product
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CL_UNIT Level
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CL_FREQ Q
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Note Table 1.1.6. Real Gross Domestic Product, Ch...
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TIME_PERIOD
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2018-01-01 18437128
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2018-04-01 18565696
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2018-07-01 18699748
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2018-10-01 18733740
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2019-01-01 18835412
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The code snippet for exporting the whole dataset, or its filtered down subset,
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can be generated by using the Export -> Pandas Python3 functionality
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.. code-block:: ipython
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import os
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import pandas_datareader.data as web
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In [1]: import os
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df = web.DataReader('dataset=NAMQ_10_GDP&v=Geopolitical entity (reporting)&h=TIME&from=2018-05-01&to=2021-01-01&GEO=[AL,AT,BE,BA,BG,HR,CY,CZ,DK,EE,EA19,FI,FR,DE,EL,HU,IS,IE,IT,XK,LV,LT,LU,MT,ME,NL,MK,NO,PL,PT,RO,RS,SK,SI,ES,SE,CH,TR,UK]&NA_ITEM=[B1GQ]&S_ADJ=[SCA]&UNIT=[CLV10_MNAC]', 'econdb')
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df.columns
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In [2]: import pandas_datareader as pdr
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In [3]: df = pdr.get_data_econdb('dataset=NAMQ_10_GDP&v=Geopolitical entity (reporting)'
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'&h=TIME&from=2018-05-01&to=2021-01-01'
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'&GEO=[UK,ES,IT,DE,FR,CH,AT]&NA_ITEM=[B1GQ]'
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'&S_ADJ=[SCA]&UNIT=[CLV10_MNAC]')
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In [4]: df.head()
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Out[4]:
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Frequency Quarterly ...
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Unit of measure Chain linked volumes (2010), million units of national currency ...
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Seasonal adjustment Seasonally and calendar adjusted data ...
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National accounts indicator (ESA10) Gross domestic product at market prices ...
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Geopolitical entity (reporting) Austria ... Switzerland
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TIME_PERIOD ...
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2018-07-01 83427 ... 181338
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2018-10-01 84268 ... 181767
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2019-01-01 84919 ... 182039
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2019-04-01 84476 ... 182848
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2019-07-01 84822 ... 183866
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In both cases, metadata for the requested Econdb series or dataset
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is in the ``MultiIndex`` columns of the returned ``DataFrame``,
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and can be conveniently converted to a ``dict`` as demonstrated below
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.. code-block:: ipython
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In [5]: meta = df.columns.to_frame().iloc[0].to_dict() # first column, positionally
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Out[5]: meta
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{'Frequency': 'Quarterly',
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'Unit of measure': 'Chain linked volumes (2010), million units of national currency',
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'Seasonal adjustment': 'Seasonally and calendar adjusted data',
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'National accounts indicator (ESA10)': 'Gross domestic product at market prices',
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'Geopolitical entity (reporting)': 'Austria'}
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Datasets can be located through Econdb's `search <https://www.econdb.com/search>`__
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engine, or discovered by exploring the `tree <https://www.econdb.com/tree/>`__
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of available statistical sources.
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.. _remote_data.enigma:
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Enigma

‎pandas_datareader/__init__.py

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import os
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import sys
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from ._version import get_versions
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from ._version import __version__
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from .data import (
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DataReader,
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Options,
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get_components_yahoo,
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get_dailysummary_iex,
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get_data_alphavantage,
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get_data_econdb,
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get_data_enigma,
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get_data_famafrench,
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get_data_fred,
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PKG = os.path.dirname(__file__)
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__version__ = get_versions()["version"]
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del get_versions
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__all__ = [
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"__version__",
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"get_components_yahoo",
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"get_data_econdb",
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"get_data_enigma",
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"get_data_famafrench",
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"get_data_yahoo",

‎pandas_datareader/_utils.py

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try:
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start = to_datetime(start)
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end = to_datetime(end)
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except (TypeError, ValueError):
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raise ValueError("Invalid date format.")
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except (TypeError, ValueError) as exc:
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raise ValueError("Invalid date format.") from exc
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if start > end:
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raise ValueError("start must be an earlier date than end")
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return start, end

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