Bug Fix: #60343 Construction of Series / Index fails from dict keys when "str" dtype is specified explicitly

.gitignore

@@ -137,3 +137,7 @@ doc/source/savefig/
 # Interactive terminal generated files #
 ########################################
 .jupyterlite.doit.db
+
+# Ignore virtual environments
+pandas-env/
+*.pyc

doc/source/getting_started/comparison/comparison_with_sas.rst

@@ -110,7 +110,7 @@ SAS provides ``PROC IMPORT`` to read csv data into a data set.
 The pandas method is :func:`read_csv`, which works similarly.
 
 .. ipython:: python
-
+   import pandas as pd
    url = (
        "https://raw.githubusercontent.com/pandas-dev/"
        "pandas/main/pandas/tests/io/data/csv/tips.csv"
@@ -523,7 +523,7 @@ the first entry for each.
 In pandas this would be written as:
 
 .. ipython:: python
-
+   import pandas as pd
    tips.groupby(["sex", "smoker"]).first()
 
 

doc/source/getting_started/comparison/comparison_with_spreadsheets.rst

@@ -98,7 +98,7 @@ dataset from the pandas tests, which is a CSV file. In Excel, you would download
 In pandas, you pass the URL or local path of the CSV file to :func:`~pandas.read_csv`:
 
 .. ipython:: python
-
+   import pandas as pd
    url = (
        "https://raw.githubusercontent.com/pandas-dev"
        "/pandas/main/pandas/tests/io/data/csv/tips.csv"
@@ -379,7 +379,7 @@ entering the first two or three values and then dragging.
 This can be achieved by creating a series and assigning it to the desired cells.
 
 .. ipython:: python
-
+    import pandas as pd
     df = pd.DataFrame({"AAA": [1] * 8, "BBB": list(range(0, 8))})
     df
 
@@ -397,7 +397,7 @@ Excel has built-in functionality for `removing duplicate values <https://support
 This is supported in pandas via :meth:`~DataFrame.drop_duplicates`.
 
 .. ipython:: python
-
+    import pandas as pd
     df = pd.DataFrame(
         {
             "class": ["A", "A", "A", "B", "C", "D"],
@@ -426,7 +426,7 @@ In Excel, we use the following configuration for the PivotTable:
 The equivalent in pandas:
 
 .. ipython:: python
-
+    import pandas as pd
     pd.pivot_table(
         tips, values="tip", index=["size"], columns=["sex"], aggfunc=np.average
     )
@@ -438,7 +438,7 @@ Adding a row
 Assuming we are using a :class:`~pandas.RangeIndex` (numbered ``0``, ``1``, etc.), we can use :func:`concat` to add a row to the bottom of a ``DataFrame``.
 
 .. ipython:: python
-
+    import pandas as pd
     df
     new_row = pd.DataFrame([["E", 51, True]],
                            columns=["class", "student_count", "all_pass"])
@@ -453,13 +453,13 @@ takes you to cells that match, one by one. In pandas, this operation is generall
 entire column or ``DataFrame`` at once through :ref:`conditional expressions <10min_tut_03_subset.rows_and_columns>`.
 
 .. ipython:: python
-
+    import pandas as pd
     tips
     tips == "Sun"
     tips["day"].str.contains("S")
 
 pandas' :meth:`~DataFrame.replace` is comparable to Excel's ``Replace All``.
 
 .. ipython:: python
-
+    import pandas as pd
     tips.replace("Thu", "Thursday")

doc/source/getting_started/comparison/comparison_with_sql.rst

@@ -15,7 +15,7 @@ the data into a DataFrame called ``tips`` and assume we have a database table of
 structure.
 
 .. ipython:: python
-
+    import pandas as pd
     url = (
         "https://raw.githubusercontent.com/pandas-dev"
         "/pandas/main/pandas/tests/io/data/csv/tips.csv"
@@ -43,7 +43,7 @@ to select all columns):
 With pandas, column selection is done by passing a list of column names to your DataFrame:
 
 .. ipython:: python
-
+    import pandas as pd
     tips[["total_bill", "tip", "smoker", "time"]]
 
 Calling the DataFrame without the list of column names would display all columns (akin to SQL's
@@ -59,7 +59,7 @@ In SQL, you can add a calculated column:
 With pandas, you can use the :meth:`DataFrame.assign` method of a DataFrame to append a new column:
 
 .. ipython:: python
-
+    import pandas as pd
     tips.assign(tip_rate=tips["tip"] / tips["total_bill"])
 
 WHERE
@@ -86,7 +86,7 @@ Tips of more than $5 at Dinner meals:
     WHERE time = 'Dinner' AND tip > 5.00;
 
 .. ipython:: python
-
+    import pandas as pd
     tips[(tips["time"] == "Dinner") & (tips["tip"] > 5.00)]
 
 Tips by parties of at least 5 diners OR bill total was more than $45:
@@ -98,14 +98,14 @@ Tips by parties of at least 5 diners OR bill total was more than $45:
     WHERE size >= 5 OR total_bill > 45;
 
 .. ipython:: python
-
+    import pandas as pd
     tips[(tips["size"] >= 5) | (tips["total_bill"] > 45)]
 
 NULL checking is done using the :meth:`~pandas.Series.notna` and :meth:`~pandas.Series.isna`
 methods.
 
 .. ipython:: python
-
+    import pandas as pd
     frame = pd.DataFrame(
         {"col1": ["A", "B", np.nan, "C", "D"], "col2": ["F", np.nan, "G", "H", "I"]}
     )
@@ -133,7 +133,7 @@ Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series.
     WHERE col1 IS NOT NULL;
 
 .. ipython:: python
-
+    import pandas as pd
     frame[frame["col1"].notna()]
 
 
@@ -170,14 +170,14 @@ Notice that in the pandas code we used :meth:`.DataFrameGroupBy.size` and not
 the number of ``NOT NULL`` records within each.
 
 .. ipython:: python
-
+    import pandas as pd
     tips.groupby("sex").count()
 
 Alternatively, we could have applied the :meth:`.DataFrameGroupBy.count` method
 to an individual column:
 
 .. ipython:: python
-
+    import pandas as pd
     tips.groupby("sex")["total_bill"].count()
 
 Multiple functions can also be applied at once. For instance, say we'd like to see how tip amount
@@ -197,7 +197,7 @@ to your grouped DataFrame, indicating which functions to apply to specific colum
     */
 
 .. ipython:: python
-
+    import pandas as pd
     tips.groupby("day").agg({"tip": "mean", "day": "size"})
 
 Grouping by more than one column is done by passing a list of columns to the
@@ -221,7 +221,7 @@ Grouping by more than one column is done by passing a list of columns to the
     */
 
 .. ipython:: python
-
+    import pandas as pd
     tips.groupby(["smoker", "day"]).agg({"tip": ["size", "mean"]})
 
 .. _compare_with_sql.join:
@@ -240,7 +240,7 @@ parameters allowing you to specify the type of join to perform (``LEFT``, ``RIGH
     join behaviour and can lead to unexpected results.
 
 .. ipython:: python
-
+    import pandas as pd
     df1 = pd.DataFrame({"key": ["A", "B", "C", "D"], "value": np.random.randn(4)})
     df2 = pd.DataFrame({"key": ["B", "D", "D", "E"], "value": np.random.randn(4)})
 
@@ -258,15 +258,15 @@ INNER JOIN
       ON df1.key = df2.key;
 
 .. ipython:: python
-
+    import pandas as pd
     # merge performs an INNER JOIN by default
     pd.merge(df1, df2, on="key")
 
 :meth:`~pandas.merge` also offers parameters for cases when you'd like to join one DataFrame's
 column with another DataFrame's index.
 
 .. ipython:: python
-
+    import pandas as pd
     indexed_df2 = df2.set_index("key")
     pd.merge(df1, indexed_df2, left_on="key", right_index=True)
 
@@ -283,7 +283,7 @@ Show all records from ``df1``.
       ON df1.key = df2.key;
 
 .. ipython:: python
-
+    import pandas as pd
     pd.merge(df1, df2, on="key", how="left")
 
 RIGHT JOIN
@@ -299,7 +299,7 @@ Show all records from ``df2``.
       ON df1.key = df2.key;
 
 .. ipython:: python
-
+    import pandas as pd
     pd.merge(df1, df2, on="key", how="right")
 
 FULL JOIN
@@ -327,7 +327,7 @@ UNION
 ``UNION ALL`` can be performed using :meth:`~pandas.concat`.
 
 .. ipython:: python
-
+    import pandas as pd
     df1 = pd.DataFrame(
         {"city": ["Chicago", "San Francisco", "New York City"], "rank": range(1, 4)}
     )
@@ -353,7 +353,7 @@ UNION
     */
 
 .. ipython:: python
-
+    import pandas as pd
     pd.concat([df1, df2])
 
 SQL's ``UNION`` is similar to ``UNION ALL``, however ``UNION`` will remove duplicate rows.
@@ -379,7 +379,7 @@ In pandas, you can use :meth:`~pandas.concat` in conjunction with
 :meth:`~pandas.DataFrame.drop_duplicates`.
 
 .. ipython:: python
-
+    import pandas as pd
     pd.concat([df1, df2]).drop_duplicates()
 
 
@@ -392,7 +392,7 @@ LIMIT
     LIMIT 10;
 
 .. ipython:: python
-
+    import pandas as pd
     tips.head(10)
 
 
@@ -410,7 +410,7 @@ Top n rows with offset
     LIMIT 10 OFFSET 5;
 
 .. ipython:: python
-
+    import pandas as pd
     tips.nlargest(10 + 5, columns="tip").tail(10)
 
 Top n rows per group
@@ -430,7 +430,7 @@ Top n rows per group
 
 
 .. ipython:: python
-
+    import pandas as pd
     (
         tips.assign(
             rn=tips.sort_values(["total_bill"], ascending=False)
@@ -445,7 +445,7 @@ Top n rows per group
 the same using ``rank(method='first')`` function
 
 .. ipython:: python
-
+    import pandas as pd
     (
         tips.assign(
             rnk=tips.groupby(["day"])["total_bill"].rank(
@@ -475,7 +475,7 @@ Notice that when using ``rank(method='min')`` function
 (as Oracle's ``RANK()`` function)
 
 .. ipython:: python
-
+    import pandas as pd
     (
         tips[tips["tip"] < 2]
         .assign(rnk_min=tips.groupby(["sex"])["tip"].rank(method="min"))
@@ -494,7 +494,7 @@ UPDATE
     WHERE tip < 2;
 
 .. ipython:: python
-
+    import pandas as pd
     tips.loc[tips["tip"] < 2, "tip"] *= 2
 
 DELETE
@@ -508,5 +508,5 @@ DELETE
 In pandas we select the rows that should remain instead of deleting the rows that should be removed:
 
 .. ipython:: python
-
+    import pandas as pd
     tips = tips.loc[tips["tip"] <= 9]

doc/source/getting_started/comparison/includes/case.rst

@@ -2,7 +2,7 @@ The equivalent pandas methods are :meth:`Series.str.upper`, :meth:`Series.str.lo
 :meth:`Series.str.title`.
 
 .. ipython:: python
-
+   import pandas as pd
    firstlast = pd.DataFrame({"string": ["John Smith", "Jane Cook"]})
    firstlast["upper"] = firstlast["string"].str.upper()
    firstlast["lower"] = firstlast["string"].str.lower()

doc/source/getting_started/comparison/includes/column_operations.rst

@@ -3,7 +3,7 @@ pandas provides vectorized operations by specifying the individual ``Series`` in
 a column from the ``DataFrame``.
 
 .. ipython:: python
-
+   import pandas as pd
    tips["total_bill"] = tips["total_bill"] - 2
    tips["new_bill"] = tips["total_bill"] / 2
    tips

doc/source/getting_started/comparison/includes/column_selection.rst

@@ -4,19 +4,19 @@ Keep certain columns
 ''''''''''''''''''''
 
 .. ipython:: python
-
+   import pandas as pd
    tips[["sex", "total_bill", "tip"]]
 
 Drop a column
 '''''''''''''
 
 .. ipython:: python
-
+   import pandas as pd
    tips.drop("sex", axis=1)
 
 Rename a column
 '''''''''''''''
 
 .. ipython:: python
-
+   import pandas as pd
    tips.rename(columns={"total_bill": "total_bill_2"})

doc/source/getting_started/comparison/includes/construct_dataframe.rst

@@ -4,6 +4,6 @@ a Python dictionary, where the keys are the column names
 and the values are the data.
 
 .. ipython:: python
-
+   import pandas as pd
    df = pd.DataFrame({"x": [1, 3, 5], "y": [2, 4, 6]})
    df

doc/source/getting_started/comparison/includes/extract_substring.rst

@@ -3,5 +3,5 @@ from a string by position locations. Keep in mind that Python
 indexes are zero-based.
 
 .. ipython:: python
-
+   import pandas as pd
    tips["sex"].str[0:1]

doc/source/getting_started/comparison/includes/filtering.rst

@@ -2,14 +2,14 @@ DataFrames can be filtered in multiple ways; the most intuitive of which is usin
 :ref:`boolean indexing <indexing.boolean>`.
 
 .. ipython:: python
-
+   import pandas as pd
    tips[tips["total_bill"] > 10]
 
 The above statement is simply passing a ``Series`` of ``True``/``False`` objects to the DataFrame,
 returning all rows with ``True``.
 
 .. ipython:: python
-
+    import pandas as pd
     is_dinner = tips["time"] == "Dinner"
     is_dinner
     is_dinner.value_counts()

doc/source/getting_started/comparison/includes/find_substring.rst

@@ -4,5 +4,5 @@ method returns its position. If not found, it returns ``-1``. Keep in mind that
 zero-based.
 
 .. ipython:: python
-
+   import pandas as pd
    tips["sex"].str.find("ale")