DEV: remove downstream test packages from environment.yml (#50157)

* DEV: remove downstream test packages from environment.yml

* undo python change, add seaborn-base

* typo

* use plain code block for statsmodels whatsnew note

* use code-block in user guide

* fixup 0.16.2 whatsnew

* also remove pandas-gbq

Co-authored-by: Patrick Hoefler <[email protected]>
Co-authored-by: MarcoGorelli <>
Co-authored-by: Marco Edward Gorelli <[email protected]>

Author: 3 people

ci/deps/actions-38-downstream_compat.yaml

@@ -39,7 +39,6 @@ dependencies:
   - numexpr
   - openpyxl
   - odfpy
-  - pandas-gbq
   - psycopg2
   - pyarrow<10
   - pymysql
@@ -68,5 +67,6 @@ dependencies:
   - statsmodels
   - coverage
   - pandas-datareader
+  - pandas-gbq
   - pyyaml
   - py

doc/source/user_guide/basics.rst

@@ -827,20 +827,54 @@ In this case, provide ``pipe`` with a tuple of ``(callable, data_keyword)``.
 
 For example, we can fit a regression using statsmodels. Their API expects a formula first and a ``DataFrame`` as the second argument, ``data``. We pass in the function, keyword pair ``(sm.ols, 'data')`` to ``pipe``:
 
-.. ipython:: python
-   :okwarning:
-
-   import statsmodels.formula.api as sm
-
-   bb = pd.read_csv("data/baseball.csv", index_col="id")
+.. code-block:: ipython
 
-   (
-       bb.query("h > 0")
-       .assign(ln_h=lambda df: np.log(df.h))
-       .pipe((sm.ols, "data"), "hr ~ ln_h + year + g + C(lg)")
-       .fit()
-       .summary()
-   )
+   In [147]: import statsmodels.formula.api as sm
+
+   In [148]: bb = pd.read_csv("data/baseball.csv", index_col="id")
+
+   In [149]: (
+      .....:     bb.query("h > 0")
+      .....:     .assign(ln_h=lambda df: np.log(df.h))
+      .....:     .pipe((sm.ols, "data"), "hr ~ ln_h + year + g + C(lg)")
+      .....:     .fit()
+      .....:     .summary()
+      .....: )
+      .....:
+   Out[149]:
+   <class 'statsmodels.iolib.summary.Summary'>
+   """
+                              OLS Regression Results
+   ==============================================================================
+   Dep. Variable:                     hr   R-squared:                       0.685
+   Model:                            OLS   Adj. R-squared:                  0.665
+   Method:                 Least Squares   F-statistic:                     34.28
+   Date:                Tue, 22 Nov 2022   Prob (F-statistic):           3.48e-15
+   Time:                        05:34:17   Log-Likelihood:                -205.92
+   No. Observations:                  68   AIC:                             421.8
+   Df Residuals:                      63   BIC:                             432.9
+   Df Model:                           4
+   Covariance Type:            nonrobust
+   ===============================================================================
+                     coef    std err          t      P>|t|      [0.025      0.975]
+   -------------------------------------------------------------------------------
+   Intercept   -8484.7720   4664.146     -1.819      0.074   -1.78e+04     835.780
+   C(lg)[T.NL]    -2.2736      1.325     -1.716      0.091      -4.922       0.375
+   ln_h           -1.3542      0.875     -1.547      0.127      -3.103       0.395
+   year            4.2277      2.324      1.819      0.074      -0.417       8.872
+   g               0.1841      0.029      6.258      0.000       0.125       0.243
+   ==============================================================================
+   Omnibus:                       10.875   Durbin-Watson:                   1.999
+   Prob(Omnibus):                  0.004   Jarque-Bera (JB):               17.298
+   Skew:                           0.537   Prob(JB):                     0.000175
+   Kurtosis:                       5.225   Cond. No.                     1.49e+07
+   ==============================================================================
+
+   Notes:
+   [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
+   [2] The condition number is large, 1.49e+07. This might indicate that there are
+   strong multicollinearity or other numerical problems.
+   """
 
 The pipe method is inspired by unix pipes and more recently dplyr_ and magrittr_, which
 have introduced the popular ``(%>%)`` (read pipe) operator for R_.

doc/source/whatsnew/v0.16.2.rst

@@ -61,21 +61,55 @@ In the example above, the functions ``f``, ``g``, and ``h`` each expected the Da
 When the function you wish to apply takes its data anywhere other than the first argument, pass a tuple
 of ``(function, keyword)`` indicating where the DataFrame should flow. For example:
 
-.. ipython:: python
-   :okwarning:
-
-   import statsmodels.formula.api as sm
-
-   bb = pd.read_csv("data/baseball.csv", index_col="id")
-
-   # sm.ols takes (formula, data)
-   (
-       bb.query("h > 0")
-       .assign(ln_h=lambda df: np.log(df.h))
-       .pipe((sm.ols, "data"), "hr ~ ln_h + year + g + C(lg)")
-       .fit()
-       .summary()
-   )
+.. code-block:: ipython
+
+    In [1]: import statsmodels.formula.api as sm
+
+    In [2]: bb = pd.read_csv("data/baseball.csv", index_col="id")
+
+    # sm.ols takes (formula, data)
+    In [3]: (
+    ...:     bb.query("h > 0")
+    ...:     .assign(ln_h=lambda df: np.log(df.h))
+    ...:     .pipe((sm.ols, "data"), "hr ~ ln_h + year + g + C(lg)")
+    ...:     .fit()
+    ...:     .summary()
+    ...: )
+    ...:
+    Out[3]:
+    <class 'statsmodels.iolib.summary.Summary'>
+    """
+                                OLS Regression Results
+    ==============================================================================
+    Dep. Variable:                     hr   R-squared:                       0.685
+    Model:                            OLS   Adj. R-squared:                  0.665
+    Method:                 Least Squares   F-statistic:                     34.28
+    Date:                Tue, 22 Nov 2022   Prob (F-statistic):           3.48e-15
+    Time:                        05:35:23   Log-Likelihood:                -205.92
+    No. Observations:                  68   AIC:                             421.8
+    Df Residuals:                      63   BIC:                             432.9
+    Df Model:                           4
+    Covariance Type:            nonrobust
+    ===============================================================================
+                    coef    std err          t      P>|t|      [0.025      0.975]
+    -------------------------------------------------------------------------------
+    Intercept   -8484.7720   4664.146     -1.819      0.074   -1.78e+04     835.780
+    C(lg)[T.NL]    -2.2736      1.325     -1.716      0.091      -4.922       0.375
+    ln_h           -1.3542      0.875     -1.547      0.127      -3.103       0.395
+    year            4.2277      2.324      1.819      0.074      -0.417       8.872
+    g               0.1841      0.029      6.258      0.000       0.125       0.243
+    ==============================================================================
+    Omnibus:                       10.875   Durbin-Watson:                   1.999
+    Prob(Omnibus):                  0.004   Jarque-Bera (JB):               17.298
+    Skew:                           0.537   Prob(JB):                     0.000175
+    Kurtosis:                       5.225   Cond. No.                     1.49e+07
+    ==============================================================================
+
+    Notes:
+    [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
+    [2] The condition number is large, 1.49e+07. This might indicate that there are
+    strong multicollinearity or other numerical problems.
+    """
 
 The pipe method is inspired by unix pipes, which stream text through
 processes. More recently dplyr_ and magrittr_ have introduced the

environment.yml

@@ -17,6 +17,7 @@ dependencies:
   - psutil
   - pytest-asyncio>=0.17
   - boto3
+  - coverage
 
   # required dependencies
   - python-dateutil
@@ -27,20 +28,22 @@ dependencies:
   - beautifulsoup4
   - blosc
   - brotlipy
+  - botocore
   - bottleneck
   - fastparquet
   - fsspec
   - html5lib
   - hypothesis
   - gcsfs
+  - ipython
   - jinja2
   - lxml
   - matplotlib>=3.6.1
   - numba>=0.53.1
   - numexpr>=2.8.0  # pin for "Run checks on imported code" job
   - openpyxl
   - odfpy
-  - pandas-gbq
+  - py
   - psycopg2
   - pyarrow<10
   - pymysql
@@ -60,17 +63,8 @@ dependencies:
 
   # downstream packages
   - aiobotocore<2.0.0  # GH#44311 pinned to fix docbuild
-  - botocore
-  - cftime
-  - dask
-  - ipython
-  - seaborn
-  - scikit-learn
-  - statsmodels
-  - coverage
-  - pandas-datareader
-  - pyyaml
-  - py
+  - dask-core
+  - seaborn-base
 
   # local testing dependencies
   - moto

requirements-dev.txt

@@ -10,26 +10,29 @@ pytest-xdist>=1.31
 psutil
 pytest-asyncio>=0.17
 boto3
+coverage
 python-dateutil
 numpy
 pytz
 beautifulsoup4
 blosc
 brotlipy
+botocore
 bottleneck
 fastparquet
 fsspec
 html5lib
 hypothesis
 gcsfs
+ipython
 jinja2
 lxml
 matplotlib>=3.6.1
 numba>=0.53.1
 numexpr>=2.8.0
 openpyxl
 odfpy
-pandas-gbq
+py
 psycopg2-binary
 pyarrow<10
 pymysql
@@ -47,17 +50,8 @@ xlrd
 xlsxwriter
 zstandard
 aiobotocore<2.0.0
-botocore
-cftime
 dask
-ipython
 seaborn
-scikit-learn
-statsmodels
-coverage
-pandas-datareader
-pyyaml
-py
 moto
 flask
 asv

scripts/generate_pip_deps_from_conda.py

@@ -24,8 +24,9 @@
 REMAP_VERSION = {"tzdata": "2022.1"}
 RENAME = {
     "pytables": "tables",
-    "geopandas-base": "geopandas",
     "psycopg2": "psycopg2-binary",
+    "dask-core": "dask",
+    "seaborn-base": "seaborn",
 }