docs: fix typos (#1093)

Found via `codespell -S .mypy_cache -L bu,fo,nd,onw,ore,te,hould,ect`

README.md

@@ -38,7 +38,7 @@ For each column, the following information (whenever relevant for the column typ
 - **Missing values**: through counts, matrix, heatmap and dendrograms
 - **Duplicate rows**: list of the most common duplicated rows
 - **Text analysis**: most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic)
-- **File and Image analysis**: file sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata
+- **File and Image analysis**: file sizes, creation dates, dimensions, indication of truncated images and existence of EXIF metadata
 
 The report contains three additional sections:
 
@@ -182,7 +182,7 @@ You need [Python 3](https://python3statement.org/) to run the package. Other dep
 
 ## 📝 Use cases
 
-The documentation includes guides, tips and tricks for tackling commmon use cases:
+The documentation includes guides, tips and tricks for tackling common use cases:
 
 | Use case | Description |
 |---|---|

docsrc/source/pages/getting_started/overview.rst

@@ -41,7 +41,7 @@ For each column, the following information (whenever relevant for the column typ
 * **Missing values**: through counts, matrix, heatmap and dendrograms
 * **Duplicate rows**: list of the most common duplicated rows
 * **Text analysis**: most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic)
-* **File and Image analysis**: file sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata
+* **File and Image analysis**: file sizes, creation dates, dimensions, indication of truncated images and existence of EXIF metadata
 
 
 The report contains three additional sections: 

docsrc/source/pages/integrations/pipelines.rst

@@ -20,7 +20,7 @@ Integration with Airflow can be easily achieved through the `BashOperator <https
 
 .. code-block:: python
 
-  # Using the Python inferface
+  # Using the Python interface
   import pandas_profiling
 
 

docsrc/source/pages/reference/announcements/2020-05-12-release-v2-8-0.rst

@@ -1,7 +1,7 @@
 Version v2.8.0 released
 -----------------------
 
-News for users working with image datasets: ``pandas-profiling`` now has build-in supports for Files and Images.
+News for users working with image datasets: ``pandas-profiling`` now has built-in supports for Files and Images.
 Moreover, the text analysis features have also been reworked, providing more informative statistics.
 
 For a better feel, have a look at the `examples <https://pandas-profiling.github.io/pandas-profiling/docs/master/pages/examples.html#showcasing-specific-features>`_ section in the docs or read the changelog for a complete view of the changes.

docsrc/source/pages/support_contrib/common_issues.rst

@@ -25,7 +25,7 @@ Conda installation defaults to v1.4.1
 Some users experience that ``conda install -c conda-forge pandas-profiling`` defaults to 1.4.1.
 
 If creating a new environment with a fresh installation does not resolve this issue, or the current environment must be kept, installing a specific version is one alternative to try: ``conda install -c conda-forge pandas-profiling=3.2.0``. 
-If it fails with an ``UnsatisfiableError`` that suggests dependant packages are either missing or incompatible, then further intervention is required to resolve the *environment* issue. However, *conda* error messages in this regard may be too cryptic or insufficient to pinpoint the culprit, therefore you may have to resort to an alternate means of troubleshooting e.g using the `Mamba Package Manager <https://github.com/mamba-org/mamba.git>`_.
+If it fails with an ``UnsatisfiableError`` that suggests dependent packages are either missing or incompatible, then further intervention is required to resolve the *environment* issue. However, *conda* error messages in this regard may be too cryptic or insufficient to pinpoint the culprit, therefore you may have to resort to an alternate means of troubleshooting e.g using the `Mamba Package Manager <https://github.com/mamba-org/mamba.git>`_.
 For an illustration of this approach see `this issue <https://github.com/pandas-profiling/pandas-profiling/issues/655>`_.
 
 Related GitHub issues: 

docsrc/source/pages/use_cases/sensitive_data.rst

@@ -8,7 +8,7 @@ In certain data-sensitive contexts (for instance, private health records), shari
 
   report = df.profile_report(sensitive=True)
 
-Additionaly, ``pandas-profiling`` does not send data to external services, making it suitable for private data.
+Additionally, ``pandas-profiling`` does not send data to external services, making it suitable for private data.
 
 Sample and duplicates
 ---------------------

src/pandas_profiling/utils/dataframe.py

@@ -173,7 +173,7 @@ def expand_mixed(df: pd.DataFrame, types: Any = None) -> pd.DataFrame:
             # Add recursion
             expanded = expand_mixed(expanded)
 
-            # Drop te expanded
+            # Drop the expanded
             df.drop(columns=[column_name], inplace=True)
 
             df = pd.concat([df, expanded], axis=1)

tests/unit/test_time_series.py

@@ -38,7 +38,7 @@ def test_timeseries_identification(html_profile: str):
     assert "<th>TimeSeries</th>" in html_profile, "TimeSeries not detected"
     assert (
         "<tr><th>TimeSeries</th><td>8</td></tr>" in html_profile
-    ), "TimeSeries incorrecly indentified"
+    ), "TimeSeries incorrectly identified"
 
 
 def test_timeseries_autocorrelation_tab(html_profile: str):
@@ -47,13 +47,13 @@ def test_timeseries_autocorrelation_tab(html_profile: str):
     ), "TimeSeries not detected"
     assert (
         html_profile.count("role=tab data-toggle=tab>Autocorrelation<") == 8
-    ), "TimeSeries autocorrelation tabs incorrecly generated"
+    ), "TimeSeries autocorrelation tabs incorrectly generated"
 
 
 def test_timeseries_seasonality(html_profile: str):
     assert (
         html_profile.count("<code>SEASONAL</code>") == 2
-    ), "Seasonality incorrecly indentified"
+    ), "Seasonality incorrectly identified"
     assert (
         html_profile.count(">Seasonal</span>") == 2
-    ), "Seasonality warning incorrecly indentified"
+    ), "Seasonality warning incorrectly identified"