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‎.pep8speaks.yml

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@@ -11,10 +11,10 @@ pycodestyle:
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max-line-length: 79
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ignore:
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- W503, # line break before binary operator
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- W504, # line break after binary operator
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- E402, # module level import not at top of file
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- E722, # do not use bare except
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- E731, # do not assign a lambda expression, use a def
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- E741, # ambiguous variable name 'l'
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- C406, # Unnecessary list literal - rewrite as a dict literal.
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- C408, # Unnecessary dict call - rewrite as a literal.
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- C409 # Unnecessary list passed to tuple() - rewrite as a tuple literal.

‎asv_bench/benchmarks/indexing.py

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import numpy as np
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import pandas.util.testing as tm
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from pandas import (Series, DataFrame, MultiIndex, Int64Index, Float64Index,
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IntervalIndex, CategoricalIndex,
5+
from pandas import (Series, DataFrame, Panel, MultiIndex, Int64Index,
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Float64Index, IntervalIndex, CategoricalIndex,
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IndexSlice, concat, date_range)
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from .pandas_vb_common import Panel
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class NumericSeriesIndexing(object):

‎asv_bench/benchmarks/join_merge.py

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except ImportError:
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from pandas import ordered_merge as merge_ordered
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from .pandas_vb_common import Panel
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class Append(object):
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‎asv_bench/benchmarks/panel_ctor.py

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from pandas import DataFrame, Panel, DatetimeIndex, date_range
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from .pandas_vb_common import Panel
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class DifferentIndexes(object):
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goal_time = 0.2

‎asv_bench/benchmarks/panel_methods.py

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import numpy as np
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from pandas import Panel
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from .pandas_vb_common import Panel
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class PanelMethods(object):
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‎asv_bench/benchmarks/period.py

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@@ -119,3 +119,6 @@ def time_align(self):
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def time_intersection(self):
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self.index[:750].intersection(self.index[250:])
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def time_unique(self):
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self.index.unique()

‎ci/azure-macos-35.yaml

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@@ -8,10 +8,10 @@ dependencies:
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- html5lib
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- jinja2
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- lxml
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- matplotlib
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- matplotlib=2.2.0
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- nomkl
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- numexpr
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- numpy=1.10.4
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- numpy=1.12.0
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- openpyxl=2.5.5
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- pytables
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- python=3.5*

‎ci/azure-windows-27.yaml

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@@ -10,7 +10,7 @@ dependencies:
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- html5lib
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- jinja2=2.8
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- lxml
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- matplotlib
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- matplotlib=2.0.1
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- numexpr
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- numpy=1.12*
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- openpyxl=2.5.5

‎ci/azure/macos.yml

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strategy:
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maxParallel: 11
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matrix:
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py35_np_110:
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py35_np_120:
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ENV_FILE: ci/azure-macos-35.yaml
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CONDA_PY: "35"
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CONDA_ENV: pandas

‎ci/azure/windows-py27.yml

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strategy:
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maxParallel: 11
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matrix:
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py36_np14:
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py36_np121:
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ENV_FILE: ci/azure-windows-27.yaml
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CONDA_PY: "27"
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CONDA_ENV: pandas

‎ci/circle-27-compat.yaml

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@@ -3,18 +3,18 @@ channels:
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- defaults
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- conda-forge
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dependencies:
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- bottleneck=1.0.0
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- bottleneck=1.2.0
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- cython=0.28.2
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- jinja2=2.8
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- numexpr=2.4.4 # we test that we correctly don't use an unsupported numexpr
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- numpy=1.9.3
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- numexpr=2.6.1
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- numpy=1.12.0
1111
- openpyxl=2.5.5
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- psycopg2
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- pytables=3.2.2
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- pytables=3.4.2
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- python-dateutil=2.5.0
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- python=2.7*
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- pytz=2013b
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- scipy=0.14.0
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- scipy=0.18.1
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- sqlalchemy=0.7.8
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- xlrd=0.9.2
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- xlsxwriter=0.5.2

‎ci/code_checks.sh

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@@ -56,6 +56,11 @@ if [[ -z "$CHECK" || "$CHECK" == "lint" ]]; then
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cpplint --quiet --extensions=c,h --headers=h --recursive --filter=-readability/casting,-runtime/int,-build/include_subdir pandas/_libs/src/*.h pandas/_libs/src/parser pandas/_libs/ujson pandas/_libs/tslibs/src/datetime
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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59+
# Imports - Check formatting using isort see setup.cfg for settings
60+
MSG='Check import format using isort ' ; echo $MSG
61+
isort --recursive --check-only pandas
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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fi
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### PATTERNS ###
@@ -118,7 +123,7 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
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MSG='Doctests frame.py' ; echo $MSG
120125
pytest --doctest-modules -v pandas/core/frame.py \
121-
-k"-axes -combine -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_dict -to_stata"
126+
-k"-axes -combine -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_stata"
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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MSG='Doctests series.py' ; echo $MSG

‎ci/environment-dev.yaml

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- flake8
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- flake8-comprehensions
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- hypothesis>=3.58.0
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- isort
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- moto
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- pytest>=3.6
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- python-dateutil>=2.5.0

‎ci/requirements-optional-conda.txt

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beautifulsoup4>=4.2.1
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blosc
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bottleneck
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bottleneck>=1.2.0
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fastparquet
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feather-format
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gcsfs
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ipykernel
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jinja2
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lxml
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matplotlib
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matplotlib>=2.0.0
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nbsphinx
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numexpr
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numexpr>=2.6.1
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openpyxl=2.5.5
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pyarrow
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pymysql
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pytables
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pytables>=3.4.2
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pytest-cov
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pytest-xdist
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s3fs
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scipy
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scipy>=0.18.1
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seaborn
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sqlalchemy
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statsmodels

‎ci/requirements-optional-pip.txt

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# Do not modify directly
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beautifulsoup4>=4.2.1
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blosc
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bottleneck
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bottleneck>=1.2.0
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fastparquet
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feather-format
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gcsfs
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ipykernel
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jinja2
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lxml
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matplotlib
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matplotlib>=2.0.0
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nbsphinx
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numexpr
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numexpr>=2.6.1
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openpyxl==2.5.5
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pyarrow
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pymysql
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tables
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pytest-cov
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pytest-xdist
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s3fs
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scipy
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scipy>=0.18.1
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seaborn
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sqlalchemy
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statsmodels

‎ci/requirements_dev.txt

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flake8
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flake8-comprehensions
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hypothesis>=3.58.0
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isort
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moto
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pytest>=3.6
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python-dateutil>=2.5.0

‎ci/travis-27-locale.yaml

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- defaults
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- conda-forge
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dependencies:
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- bottleneck=1.0.0
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- bottleneck=1.2.0
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- cython=0.28.2
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- lxml
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- matplotlib=1.4.3
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- numpy=1.9.3
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- matplotlib=2.0.0
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- numpy=1.12.0
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- openpyxl=2.4.0
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- python-dateutil
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- python-blosc

‎ci/travis-27.yaml

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- jemalloc=4.5.0.post
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- jinja2=2.8
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- lxml
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- matplotlib
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- matplotlib=2.2.2
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- mock
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- nomkl
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- numexpr

‎ci/travis-36.yaml

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- geopandas
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- html5lib
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- ipython
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- isort
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- jinja2
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- lxml
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- matplotlib

‎ci/travis-37-numpydev.yaml

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- "git+git://github.com/dateutil/dateutil.git"
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- "-f https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com"
1515
- "--pre"
16-
- "numpy"
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- "numpy<=1.16.0.dev0+20181015190246"
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- "scipy"

‎doc/source/api.rst

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eval
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248+
Hashing
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~~~~~~~
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.. autosummary::
252+
:toctree: generated/
253+
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util.hash_array
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util.hash_pandas_object
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Testing
249258
~~~~~~~
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‎doc/source/cookbook.rst

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The `method` argument within `DataFrame.corr` can accept a callable in addition to the named correlation types. Here we compute the `distance correlation <https://en.wikipedia.org/wiki/Distance_correlation>`__ matrix for a `DataFrame` object.
12301230

1231-
.. ipython:: python
1232-
1233-
def distcorr(x, y):
1234-
n = len(x)
1235-
a = np.zeros(shape=(n, n))
1236-
b = np.zeros(shape=(n, n))
1237-
1238-
for i in range(n):
1239-
for j in range(i + 1, n):
1240-
a[i, j] = abs(x[i] - x[j])
1241-
b[i, j] = abs(y[i] - y[j])
1242-
1243-
a += a.T
1244-
b += b.T
1245-
1246-
a_bar = np.vstack([np.nanmean(a, axis=0)] * n)
1247-
b_bar = np.vstack([np.nanmean(b, axis=0)] * n)
1248-
1249-
A = a - a_bar - a_bar.T + np.full(shape=(n, n), fill_value=a_bar.mean())
1250-
B = b - b_bar - b_bar.T + np.full(shape=(n, n), fill_value=b_bar.mean())
1251-
1252-
cov_ab = np.sqrt(np.nansum(A * B)) / n
1253-
std_a = np.sqrt(np.sqrt(np.nansum(A**2)) / n)
1254-
std_b = np.sqrt(np.sqrt(np.nansum(B**2)) / n)
1255-
1256-
return cov_ab / std_a / std_b
1257-
1258-
df = pd.DataFrame(np.random.normal(size=(100, 3)))
1231+
.. code-block:: python
12591232
1260-
df.corr(method=distcorr)
1233+
>>> def distcorr(x, y):
1234+
... n = len(x)
1235+
... a = np.zeros(shape=(n, n))
1236+
... b = np.zeros(shape=(n, n))
1237+
...
1238+
... for i in range(n):
1239+
... for j in range(i + 1, n):
1240+
... a[i, j] = abs(x[i] - x[j])
1241+
... b[i, j] = abs(y[i] - y[j])
1242+
...
1243+
... a += a.T
1244+
... b += b.T
1245+
...
1246+
... a_bar = np.vstack([np.nanmean(a, axis=0)] * n)
1247+
... b_bar = np.vstack([np.nanmean(b, axis=0)] * n)
1248+
...
1249+
... A = a - a_bar - a_bar.T + np.full(shape=(n, n), fill_value=a_bar.mean())
1250+
... B = b - b_bar - b_bar.T + np.full(shape=(n, n), fill_value=b_bar.mean())
1251+
...
1252+
... cov_ab = np.sqrt(np.nansum(A * B)) / n
1253+
... std_a = np.sqrt(np.sqrt(np.nansum(A**2)) / n)
1254+
... std_b = np.sqrt(np.sqrt(np.nansum(B**2)) / n)
1255+
...
1256+
... return cov_ab / std_a / std_b
1257+
...
1258+
>>> df = pd.DataFrame(np.random.normal(size=(100, 3)))
1259+
...
1260+
>>> df.corr(method=distcorr)
1261+
0 1 2
1262+
0 1.000000 0.171368 0.145302
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1 0.171368 1.000000 0.189919
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2 0.145302 0.189919 1.000000
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Timedeltas
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----------

‎doc/source/install.rst

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------------
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* `setuptools <https://setuptools.readthedocs.io/en/latest/>`__: 24.2.0 or higher
228-
* `NumPy <http://www.numpy.org>`__: 1.9.0 or higher
228+
* `NumPy <http://www.numpy.org>`__: 1.12.0 or higher
229229
* `python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__: 2.5.0 or higher
230230
* `pytz <http://pytz.sourceforge.net/>`__
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237237
* `numexpr <https://github.com/pydata/numexpr>`__: for accelerating certain numerical operations.
238238
``numexpr`` uses multiple cores as well as smart chunking and caching to achieve large speedups.
239-
If installed, must be Version 2.4.6 or higher.
239+
If installed, must be Version 2.6.1 or higher.
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241241
* `bottleneck <https://github.com/kwgoodman/bottleneck>`__: for accelerating certain types of ``nan``
242242
evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups. If installed,
243-
must be Version 1.0.0 or higher.
243+
must be Version 1.2.0 or higher.
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.. note::
246246

@@ -255,9 +255,9 @@ Optional Dependencies
255255

256256
* `Cython <http://www.cython.org>`__: Only necessary to build development
257257
version. Version 0.28.2 or higher.
258-
* `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions, Version 0.14.0 or higher
258+
* `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions, Version 0.18.1 or higher
259259
* `xarray <http://xarray.pydata.org>`__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended.
260-
* `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage. Version 3.0.0 or higher required, Version 3.2.1 or higher highly recommended.
260+
* `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage, Version 3.4.2 or higher
261261
* `Feather Format <https://github.com/wesm/feather>`__: necessary for feather-based storage, version 0.3.1 or higher.
262262
* `Apache Parquet <https://parquet.apache.org/>`__, either `pyarrow <http://arrow.apache.org/docs/python/>`__ (>= 0.4.1) or `fastparquet <https://fastparquet.readthedocs.io/en/latest>`__ (>= 0.0.6) for parquet-based storage. The `snappy <https://pypi.org/project/python-snappy>`__ and `brotli <https://pypi.org/project/brotlipy>`__ are available for compression support.
263263
* `SQLAlchemy <http://www.sqlalchemy.org>`__: for SQL database support. Version 0.8.1 or higher recommended. Besides SQLAlchemy, you also need a database specific driver. You can find an overview of supported drivers for each SQL dialect in the `SQLAlchemy docs <http://docs.sqlalchemy.org/en/latest/dialects/index.html>`__. Some common drivers are:
@@ -266,7 +266,7 @@ Optional Dependencies
266266
* `pymysql <https://github.com/PyMySQL/PyMySQL>`__: for MySQL.
267267
* `SQLite <https://docs.python.org/3/library/sqlite3.html>`__: for SQLite, this is included in Python's standard library by default.
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269-
* `matplotlib <http://matplotlib.org/>`__: for plotting, Version 1.4.3 or higher.
269+
* `matplotlib <http://matplotlib.org/>`__: for plotting, Version 2.0.0 or higher.
270270
* For Excel I/O:
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272272
* `xlrd/xlwt <http://www.python-excel.org/>`__: Excel reading (xlrd) and writing (xlwt)

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