Merge branch 'main' into numeric_to_df_cum

Author: Khor Chean Wei

asv_bench/asv.conf.json

@@ -41,6 +41,7 @@
     // pip (with all the conda available packages installed first,
     // followed by the pip installed packages).
     "matrix": {
+        "pip+build": [],
         "Cython": ["3.0"],
         "matplotlib": [],
         "sqlalchemy": [],

ci/deps/actions-310.yaml

@@ -24,6 +24,8 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.11.2
+  # https://github.com/conda-forge/pytables-feedstock/issues/97
+  - c-blosc2=2.13.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2023.10.0

ci/deps/actions-311-downstream_compat.yaml

@@ -26,6 +26,8 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.11.2
+  # https://github.com/conda-forge/pytables-feedstock/issues/97
+  - c-blosc2=2.13.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2023.10.0

ci/deps/actions-311.yaml

@@ -24,6 +24,8 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.11.2
+  # https://github.com/conda-forge/pytables-feedstock/issues/97
+  - c-blosc2=2.13.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2023.10.0

ci/deps/actions-312.yaml

@@ -24,6 +24,8 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.11.2
+  # https://github.com/conda-forge/pytables-feedstock/issues/97
+  - c-blosc2=2.13.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2023.10.0

ci/deps/actions-39-minimum_versions.yaml

@@ -27,6 +27,8 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4=4.11.2
+  # https://github.com/conda-forge/pytables-feedstock/issues/97
+  - c-blosc2=2.13.2
   - blosc=1.21.3
   - bottleneck=1.3.6
   - fastparquet=2023.10.0

ci/deps/actions-39.yaml

@@ -24,6 +24,8 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.11.2
+  # https://github.com/conda-forge/pytables-feedstock/issues/97
+  - c-blosc2=2.13.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2023.10.0

ci/deps/circle-310-arm64.yaml

@@ -25,6 +25,8 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.11.2
+  # https://github.com/conda-forge/pytables-feedstock/issues/97
+  - c-blosc2=2.13.2
   - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2023.10.0

doc/source/user_guide/basics.rst

@@ -476,15 +476,15 @@ For example:
 .. ipython:: python
 
    df
-   df.mean(0)
-   df.mean(1)
+   df.mean(axis=0)
+   df.mean(axis=1)
 
 All such methods have a ``skipna`` option signaling whether to exclude missing
 data (``True`` by default):
 
 .. ipython:: python
 
-   df.sum(0, skipna=False)
+   df.sum(axis=0, skipna=False)
    df.sum(axis=1, skipna=True)
 
 Combined with the broadcasting / arithmetic behavior, one can describe various
@@ -495,8 +495,8 @@ standard deviation of 1), very concisely:
 
    ts_stand = (df - df.mean()) / df.std()
    ts_stand.std()
-   xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)
-   xs_stand.std(1)
+   xs_stand = df.sub(df.mean(axis=1), axis=0).div(df.std(axis=1), axis=0)
+   xs_stand.std(axis=1)
 
 Note that methods like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.cumprod`
 preserve the location of ``NaN`` values. This is somewhat different from

doc/source/user_guide/indexing.rst

@@ -952,7 +952,7 @@ To select a row where each column meets its own criterion:
 
   values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]}
 
-  row_mask = df.isin(values).all(1)
+  row_mask = df.isin(values).all(axis=1)
 
   df[row_mask]