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‎.github/actions/build_pandas/action.yml

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name: Build pandas
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description: Rebuilds the C extensions and installs pandas
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runs:
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using: composite
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steps:
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- name: Environment Detail
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run: |
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conda info
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conda list
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shell: bash -l {0}
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- name: Build Pandas
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run: |
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python setup.py build_ext -j 2
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python -m pip install -e . --no-build-isolation --no-use-pep517
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shell: bash -l {0}

‎.github/actions/setup/action.yml

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name: Set up pandas
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description: Runs all the setup steps required to have a built pandas ready to use
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runs:
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using: composite
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steps:
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- name: Setting conda path
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run: echo "${HOME}/miniconda3/bin" >> $GITHUB_PATH
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shell: bash -l {0}
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- name: Setup environment and build pandas
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run: ci/setup_env.sh
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shell: bash -l {0}

‎.github/workflows/ci.yml

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environment-file: ${{ env.ENV_FILE }}
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use-only-tar-bz2: true
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- name: Environment Detail
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run: |
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conda info
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conda list
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- name: Build Pandas
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run: |
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python setup.py build_ext -j 2
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python -m pip install -e . --no-build-isolation --no-use-pep517
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uses: ./.github/actions/build_pandas
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- name: Linting
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run: ci/code_checks.sh lint
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runs-on: ubuntu-latest
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steps:
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- name: Setting conda path
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run: echo "${HOME}/miniconda3/bin" >> $GITHUB_PATH
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- name: Checkout
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uses: actions/checkout@v1
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- name: Setup environment and build pandas
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run: ci/setup_env.sh
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- name: Set up pandas
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uses: ./.github/actions/setup
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- name: Build website
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run: |
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run: rsync -az --delete doc/build/html/ docs@${{ secrets.server_ip }}:/usr/share/nginx/pandas/pandas-docs/dev
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if: github.event_name == 'push'
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- name: Move docs into site directory
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run: mv doc/build/html web/build/docs
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- name: Save website as an artifact
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uses: actions/upload-artifact@v2
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with:
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name: website
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path: web/build
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retention-days: 14
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data_manager:
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name: Test experimental data manager
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runs-on: ubuntu-latest
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steps:
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- name: Setting conda path
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run: echo "${HOME}/miniconda3/bin" >> $GITHUB_PATH
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- name: Checkout
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uses: actions/checkout@v1
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- name: Setup environment and build pandas
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run: ci/setup_env.sh
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- name: Set up pandas
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uses: ./.github/actions/setup
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- name: Run tests
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run: |

‎.github/workflows/database.yml

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environment-file: ${{ env.ENV_FILE }}
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use-only-tar-bz2: true
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- name: Environment Detail
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run: |
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conda info
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conda list
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- name: Build Pandas
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run: |
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python setup.py build_ext -j 2
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python -m pip install -e . --no-build-isolation --no-use-pep517
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uses: ./.github/actions/build_pandas
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- name: Test
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run: ci/run_tests.sh
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environment-file: ${{ env.ENV_FILE }}
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use-only-tar-bz2: true
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- name: Environment Detail
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run: |
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conda info
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conda list
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- name: Build Pandas
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run: |
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python setup.py build_ext -j 2
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python -m pip install -e . --no-build-isolation --no-use-pep517
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uses: ./.github/actions/build_pandas
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- name: Test
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run: ci/run_tests.sh

‎.pre-commit-config.yaml

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hooks:
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- id: isort
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- repo: https://github.com/asottile/pyupgrade
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rev: v2.9.0
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rev: v2.10.0
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hooks:
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- id: pyupgrade
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args: [--py37-plus, --keep-runtime-typing]
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- repo: https://github.com/pre-commit/pygrep-hooks
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rev: v1.7.0
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rev: v1.7.1
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hooks:
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- id: rst-backticks
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- id: rst-directive-colons
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types: [python]
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files: ^pandas/tests/
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exclude: ^pandas/tests/extension/
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- id: unwanted-patters-pytest-xfail
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name: Check for use of pytest.xfail
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entry: pytest\.xfail
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language: pygrep
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types: [python]
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files: ^pandas/tests/
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- id: inconsistent-namespace-usage
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name: 'Check for inconsistent use of pandas namespace in tests'
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entry: python scripts/check_for_inconsistent_pandas_namespace.py

‎asv_bench/benchmarks/categoricals.py

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self.a = pd.Categorical(list("aabbcd") * N)
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self.b = pd.Categorical(list("bbcdjk") * N)
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self.idx_a = pd.CategoricalIndex(range(N), range(N))
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self.idx_b = pd.CategoricalIndex(range(N + 1), range(N + 1))
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self.df_a = pd.DataFrame(range(N), columns=["a"], index=self.idx_a)
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self.df_b = pd.DataFrame(range(N + 1), columns=["a"], index=self.idx_b)
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def time_concat(self):
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pd.concat([self.s, self.s])
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def time_union(self):
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union_categoricals([self.a, self.b])
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def time_append_overlapping_index(self):
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self.idx_a.append(self.idx_a)
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def time_append_non_overlapping_index(self):
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self.idx_a.append(self.idx_b)
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def time_concat_overlapping_index(self):
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pd.concat([self.df_a, self.df_a])
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def time_concat_non_overlapping_index(self):
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pd.concat([self.df_a, self.df_b])
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class ValueCounts:
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self.index.get_loc(self.category)
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def time_shallow_copy(self):
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self.index._shallow_copy()
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self.index._view()
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def time_align(self):
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pd.DataFrame({"a": self.series, "b": self.series[:500]})

‎asv_bench/benchmarks/dtypes.py

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import numpy as np
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import pandas as pd
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from pandas import DataFrame
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import pandas._testing as tm
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from pandas.api.types import pandas_dtype
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from pandas.api.types import is_extension_array_dtype, pandas_dtype
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from .pandas_vb_common import (
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class CheckDtypes:
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def setup(self):
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self.ext_dtype = pd.Int64Dtype()
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self.np_dtype = np.dtype("int64")
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def time_is_extension_array_dtype_true(self):
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is_extension_array_dtype(self.ext_dtype)
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def time_is_extension_array_dtype_false(self):
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is_extension_array_dtype(self.np_dtype)
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from .pandas_vb_common import setup # noqa: F401 isort:skip

‎asv_bench/benchmarks/gil.py

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import numpy as np
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from pandas import DataFrame, Series, date_range, factorize, read_csv
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from pandas.core.algorithms import take_1d
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from pandas.core.algorithms import take_nd
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from .pandas_vb_common import tm
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def parallel_take1d():
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take_1d(df["col"].values, indexer)
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take_nd(df["col"].values, indexer)
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self.parallel_take1d = parallel_take1d
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‎asv_bench/benchmarks/hash_functions.py

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class UniqueForLargePyObjectInts:
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def setup(self):
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lst = [x << 32 for x in range(5000)]
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self.arr = np.array(lst, dtype=np.object_)
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def time_unique(self):
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pd.unique(self.arr)
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class IsinWithRandomFloat:
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[np.float64, np.object],

‎asv_bench/benchmarks/period.py

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self.index._view()
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‎asv_bench/benchmarks/rolling.py

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params = ["var", "std", "cov", "corr"]
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param_names = ["method"]
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def setup(self, method):
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df = pd.DataFrame({"A": range(50), "B": range(50)})
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self.gb_ewm = df.groupby("A").ewm(com=1.0)
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def time_groupby_method(self, method):
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getattr(self.gb_ewm, method)()
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class GroupbyEWMEngine:
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‎asv_bench/benchmarks/timedelta.py

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‎doc/cheatsheet/Pandas_Cheat_Sheet.pdf

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‎doc/cheatsheet/Pandas_Cheat_Sheet.pptx

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‎doc/source/development/code_style.rst

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# wrong
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from common import test_base
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Testing
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=======
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Failing tests
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--------------
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See https://docs.pytest.org/en/latest/skipping.html for background.
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Do not use ``pytest.xfail``
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---------------------------
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Do not use this method. It has the same behavior as ``pytest.skip``, namely
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it immediately stops the test and does not check if the test will fail. If
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this is the behavior you desire, use ``pytest.skip`` instead.
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Using ``pytest.mark.xfail``
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---------------------------
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Use this method if a test is known to fail but the manner in which it fails
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is not meant to be captured. It is common to use this method for a test that
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exhibits buggy behavior or a non-implemented feature. If
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the failing test has flaky behavior, use the argument ``strict=False``. This
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will make it so pytest does not fail if the test happens to pass.
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Prefer the decorator ``@pytest.mark.xfail`` and the argument ``pytest.param``
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over usage within a test so that the test is appropriately marked during the
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collection phase of pytest. For xfailing a test that involves multiple
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parameters, a fixture, or a combination of these, it is only possible to
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xfail during the testing phase. To do so, use the ``request`` fixture:
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.. code-block:: python
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import pytest
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def test_xfail(request):
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request.node.add_marker(pytest.mark.xfail(reason="Indicate why here"))
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xfail is not to be used for tests involving failure due to invalid user arguments.
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For these tests, we need to verify the correct exception type and error message
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is being raised, using ``pytest.raises`` instead.
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Miscellaneous
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=============

‎doc/source/development/contributing.rst

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Previewing changes
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------------------
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Once, the pull request is submitted, GitHub Actions will automatically build the
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documentation. To view the built site:
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#. Wait for the ``CI / Web and docs`` check to complete.
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#. Click ``Details`` next to it.
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#. From the ``Artifacts`` drop-down, click ``docs`` or ``website`` to download
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the site as a ZIP file.
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.. _contributing.code:
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Contributing to the code base

‎doc/source/development/extending.rst

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data structure as the result of an operation. By overriding these properties,
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There are 3 constructor properties to be defined:
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There are 3 possible constructor properties to be defined on a subclass:
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* ``_constructor``: Used when a manipulation result has the same dimensions as the original.
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* ``_constructor_sliced``: Used when a manipulation result has one lower dimension(s) as the original, such as ``DataFrame`` single columns slicing.
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* ``_constructor_expanddim``: Used when a manipulation result has one higher dimension as the original, such as ``Series.to_frame()``.
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Following table shows how ``pandas`` data structures define constructor properties by default.
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=========================== ======================= =============
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Property Attributes ``Series`` ``DataFrame``
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=========================== ======================= =============
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``_constructor`` ``Series`` ``DataFrame``
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``_constructor_sliced`` ``NotImplementedError`` ``Series``
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``_constructor_expanddim`` ``DataFrame`` ``NotImplementedError``
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=========================== ======================= =============
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* ``DataFrame/Series._constructor``: Used when a manipulation result has the same dimension as the original.
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* ``DataFrame._constructor_sliced``: Used when a ``DataFrame`` (sub-)class manipulation result should be a ``Series`` (sub-)class.
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* ``Series._constructor_expanddim``: Used when a ``Series`` (sub-)class manipulation result should be a ``DataFrame`` (sub-)class, e.g. ``Series.to_frame()``.
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Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame`` overriding constructor properties.
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‎doc/source/user_guide/dsintro.rst

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.. note::
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We will address array-based indexing like ``s[[4, 3, 1]]``
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in :ref:`section <indexing>`.
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in :ref:`section on indexing <indexing>`.
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Like a NumPy array, a pandas Series has a :attr:`~Series.dtype`.
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