# Comparison with SQL

Since many potential pandas users have some familiarity with SQL, this page is meant to provide some examples of how various SQL operations would be performed using pandas.

If you’re new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library.

As is customary, we import pandas and NumPy as follows:

    In [1]: import pandas as pd

    In [2]: import numpy as np

Most of the examples will utilize the `tips` dataset found within pandas tests. We’ll read the data into a DataFrame called `tips` and assume we have a database table of the same name and structure.

    In [3]: url = (
       ...:     "https://raw.github.com/pandas-dev"
       ...:     "/pandas/master/pandas/tests/io/data/csv/tips.csv"
       ...: )
       ...:

    In [4]: tips = pd.read_csv(url)
    ---------------------------------------------------------------------------
    ConnectionRefusedError                    Traceback (most recent call last)
    /usr/lib/python3.10/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
       1347             try:
    -> 1348                 h.request(req.get_method(), req.selector, req.data, headers,
       1349                           encode_chunked=req.has_header('Transfer-encoding'))

    /usr/lib/python3.10/http/client.py in request(self, method, url, body, headers, encode_chunked)
       1281         """Send a complete request to the server."""
    -> 1282         self._send_request(method, url, body, headers, encode_chunked)
       1283

    /usr/lib/python3.10/http/client.py in _send_request(self, method, url, body, headers, encode_chunked)
       1327             body = _encode(body, 'body')
    -> 1328         self.endheaders(body, encode_chunked=encode_chunked)
       1329

    /usr/lib/python3.10/http/client.py in endheaders(self, message_body, encode_chunked)
       1276             raise CannotSendHeader()
    -> 1277         self._send_output(message_body, encode_chunked=encode_chunked)
       1278

    /usr/lib/python3.10/http/client.py in _send_output(self, message_body, encode_chunked)
       1036         del self._buffer[:]
    -> 1037         self.send(msg)
       1038

    /usr/lib/python3.10/http/client.py in send(self, data)
        974             if self.auto_open:
    --> 975                 self.connect()
        976             else:

    /usr/lib/python3.10/http/client.py in connect(self)
       1446
    -> 1447             super().connect()
       1448

    /usr/lib/python3.10/http/client.py in connect(self)
        940         sys.audit("http.client.connect", self, self.host, self.port)
    --> 941         self.sock = self._create_connection(
        942             (self.host,self.port), self.timeout, self.source_address)

    /usr/lib/python3.10/socket.py in create_connection(address, timeout, source_address)
        844         try:
    --> 845             raise err
        846         finally:

    /usr/lib/python3.10/socket.py in create_connection(address, timeout, source_address)
        832                 sock.bind(source_address)
    --> 833             sock.connect(sa)
        834             # Break explicitly a reference cycle

    ConnectionRefusedError: [Errno 111] Connection refused

    During handling of the above exception, another exception occurred:

    URLError                                  Traceback (most recent call last)
    <ipython-input-4-8ab2297b7141> in <module>
    ----> 1 tips = pd.read_csv(url)

    /usr/lib/python3/dist-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
        309                     stacklevel=stacklevel,
        310                 )
    --> 311             return func(*args, **kwargs)
        312
        313         return wrapper

    /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
        584     kwds.update(kwds_defaults)
        585
    --> 586     return _read(filepath_or_buffer, kwds)
        587
        588

    /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py in _read(filepath_or_buffer, kwds)
        480
        481     # Create the parser.
    --> 482     parser = TextFileReader(filepath_or_buffer, **kwds)
        483
        484     if chunksize or iterator:

    /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py in __init__(self, f, engine, **kwds)
        809             self.options["has_index_names"] = kwds["has_index_names"]
        810
    --> 811         self._engine = self._make_engine(self.engine)
        812
        813     def close(self):

    /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py in _make_engine(self, engine)
       1038             )
       1039         # error: Too many arguments for "ParserBase"
    -> 1040         return mapping[engine](self.f, **self.options)  # type: ignore[call-arg]
       1041
       1042     def _failover_to_python(self):

    /usr/lib/python3/dist-packages/pandas/io/parsers/c_parser_wrapper.py in __init__(self, src, **kwds)
         49
         50         # open handles
    ---> 51         self._open_handles(src, kwds)
         52         assert self.handles is not None
         53

    /usr/lib/python3/dist-packages/pandas/io/parsers/base_parser.py in _open_handles(self, src, kwds)
        220         Let the readers open IOHandles after they are done with their potential raises.
        221         """
    --> 222         self.handles = get_handle(
        223             src,
        224             "r",

    /usr/lib/python3/dist-packages/pandas/io/common.py in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
        607
        608     # open URLs
    --> 609     ioargs = _get_filepath_or_buffer(
        610         path_or_buf,
        611         encoding=encoding,

    /usr/lib/python3/dist-packages/pandas/io/common.py in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
        310         # assuming storage_options is to be interpreted as headers
        311         req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
    --> 312         with urlopen(req_info) as req:
        313             content_encoding = req.headers.get("Content-Encoding", None)
        314             if content_encoding == "gzip":

    /usr/lib/python3/dist-packages/pandas/io/common.py in urlopen(*args, **kwargs)
        210     import urllib.request
        211
    --> 212     return urllib.request.urlopen(*args, **kwargs)
        213
        214

    /usr/lib/python3.10/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
        214     else:
        215         opener = _opener
    --> 216     return opener.open(url, data, timeout)
        217
        218 def install_opener(opener):

    /usr/lib/python3.10/urllib/request.py in open(self, fullurl, data, timeout)
        517
        518         sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
    --> 519         response = self._open(req, data)
        520
        521         # post-process response

    /usr/lib/python3.10/urllib/request.py in _open(self, req, data)
        534
        535         protocol = req.type
    --> 536         result = self._call_chain(self.handle_open, protocol, protocol +
        537                                   '_open', req)
        538         if result:

    /usr/lib/python3.10/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
        494         for handler in handlers:
        495             func = getattr(handler, meth_name)
    --> 496             result = func(*args)
        497             if result is not None:
        498                 return result

    /usr/lib/python3.10/urllib/request.py in https_open(self, req)
       1389
       1390         def https_open(self, req):
    -> 1391             return self.do_open(http.client.HTTPSConnection, req,
       1392                 context=self._context, check_hostname=self._check_hostname)
       1393

    /usr/lib/python3.10/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
       1349                           encode_chunked=req.has_header('Transfer-encoding'))
       1350             except OSError as err: # timeout error
    -> 1351                 raise URLError(err)
       1352             r = h.getresponse()
       1353         except:

    URLError: <urlopen error [Errno 111] Connection refused>

    In [5]: tips
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-5-9034fb5f8272> in <module>
    ----> 1 tips

    NameError: name 'tips' is not defined

## Copies vs. in place operations

Most pandas operations return copies of the `Series`/`DataFrame`. To make the changes “stick”, you’ll need to either assign to a new variable:

>     sorted_df = df.sort_values("col1")

or overwrite the original one:

>     df = df.sort_values("col1")

Note

You will see an `inplace=True` keyword argument available for some methods:

    df.sort_values("col1", inplace=True)

Its use is discouraged. More information.

## SELECT

In SQL, selection is done using a comma-separated list of columns you’d like to select (or a `*` to select all columns):

    SELECT total_bill, tip, smoker, time
    FROM tips;

With pandas, column selection is done by passing a list of column names to your DataFrame:

    In [6]: tips[["total_bill", "tip", "smoker", "time"]]
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-6-235aea239d0c> in <module>
    ----> 1 tips[["total_bill", "tip", "smoker", "time"]]

    NameError: name 'tips' is not defined

Calling the DataFrame without the list of column names would display all columns (akin to SQL’s `*`).

In SQL, you can add a calculated column:

    SELECT *, tip/total_bill as tip_rate
    FROM tips;

With pandas, you can use the `DataFrame.assign()` method of a DataFrame to append a new column:

    In [7]: tips.assign(tip_rate=tips["tip"] / tips["total_bill"])
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-7-fbeef400aaa7> in <module>
    ----> 1 tips.assign(tip_rate=tips["tip"] / tips["total_bill"])

    NameError: name 'tips' is not defined

## WHERE

Filtering in SQL is done via a WHERE clause.

    SELECT *
    FROM tips
    WHERE time = 'Dinner';

DataFrames can be filtered in multiple ways; the most intuitive of which is using boolean indexing.

    In [8]: tips[tips["total_bill"] > 10]
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-8-4e5df2b556a0> in <module>
    ----> 1 tips[tips["total_bill"] > 10]

    NameError: name 'tips' is not defined

The above statement is simply passing a `Series` of `True`/`False` objects to the DataFrame, returning all rows with `True`.

    In [9]: is_dinner = tips["time"] == "Dinner"
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-9-a38011ae4c7d> in <module>
    ----> 1 is_dinner = tips["time"] == "Dinner"

    NameError: name 'tips' is not defined

    In [10]: is_dinner
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-10-49a5e8deec4a> in <module>
    ----> 1 is_dinner

    NameError: name 'is_dinner' is not defined

    In [11]: is_dinner.value_counts()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-11-900d99c3802f> in <module>
    ----> 1 is_dinner.value_counts()

    NameError: name 'is_dinner' is not defined

    In [12]: tips[is_dinner]
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-12-ed12d6ad643e> in <module>
    ----> 1 tips[is_dinner]

    NameError: name 'tips' is not defined

Just like SQL’s `OR` and `AND`, multiple conditions can be passed to a DataFrame using `|` (`OR`) and `&` (`AND`).

Tips of more than $5 at Dinner meals:

    SELECT *
    FROM tips
    WHERE time = 'Dinner' AND tip > 5.00;

    In [13]: tips[(tips["time"] == "Dinner") & (tips["tip"] > 5.00)]
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-13-e4cc5e25dd38> in <module>
    ----> 1 tips[(tips["time"] == "Dinner") & (tips["tip"] > 5.00)]

    NameError: name 'tips' is not defined

Tips by parties of at least 5 diners OR bill total was more than $45:

    SELECT *
    FROM tips
    WHERE size >= 5 OR total_bill > 45;

    In [14]: tips[(tips["size"] >= 5) | (tips["total_bill"] > 45)]
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-14-8798829741f8> in <module>
    ----> 1 tips[(tips["size"] >= 5) | (tips["total_bill"] > 45)]

    NameError: name 'tips' is not defined

NULL checking is done using the `notna()` and `isna()` methods.

    In [15]: frame = pd.DataFrame(
       ....:     {"col1": ["A", "B", np.NaN, "C", "D"], "col2": ["F", np.NaN, "G", "H", "I"]}
       ....: )
       ....:

    In [16]: frame
    Out[16]:
      col1 col2
    0    A    F
    1    B  NaN
    2  NaN    G
    3    C    H
    4    D    I

Assume we have a table of the same structure as our DataFrame above. We can see only the records where `col2` IS NULL with the following query:

    SELECT *
    FROM frame
    WHERE col2 IS NULL;

    In [17]: frame[frame["col2"].isna()]
    Out[17]:
      col1 col2
    1    B  NaN

Getting items where `col1` IS NOT NULL can be done with `notna()`.

    SELECT *
    FROM frame
    WHERE col1 IS NOT NULL;

    In [18]: frame[frame["col1"].notna()]
    Out[18]:
      col1 col2
    0    A    F
    1    B  NaN
    3    C    H
    4    D    I

## GROUP BY

In pandas, SQL’s `GROUP BY` operations are performed using the similarly named `groupby()` method. `groupby()` typically refers to a process where we’d like to split a dataset into groups, apply some function (typically aggregation) , and then combine the groups together.

A common SQL operation would be getting the count of records in each group throughout a dataset. For instance, a query getting us the number of tips left by sex:

    SELECT sex, count(*)
    FROM tips
    GROUP BY sex;
    /*
    Female     87
    Male      157
    */

The pandas equivalent would be:

    In [19]: tips.groupby("sex").size()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-19-afb60da9b6c0> in <module>
    ----> 1 tips.groupby("sex").size()

    NameError: name 'tips' is not defined

Notice that in the pandas code we used `size()` and not `count()`. This is because `count()` applies the function to each column, returning the number of `NOT NULL` records within each.

    In [20]: tips.groupby("sex").count()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-20-30ff3551b8c4> in <module>
    ----> 1 tips.groupby("sex").count()

    NameError: name 'tips' is not defined

Alternatively, we could have applied the `count()` method to an individual column:

    In [21]: tips.groupby("sex")["total_bill"].count()
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-21-7fdfd21f708d> in <module>
    ----> 1 tips.groupby("sex")["total_bill"].count()

    NameError: name 'tips' is not defined

Multiple functions can also be applied at once. For instance, say we’d like to see how tip amount differs by day of the week - `agg()` allows you to pass a dictionary to your grouped DataFrame, indicating which functions to apply to specific columns.

    SELECT day, AVG(tip), COUNT(*)
    FROM tips
    GROUP BY day;
    /*
    Fri   2.734737   19
    Sat   2.993103   87
    Sun   3.255132   76
    Thu  2.771452   62
    */

    In [22]: tips.groupby("day").agg({"tip": np.mean, "day": np.size})
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-22-b16929e2edee> in <module>
    ----> 1 tips.groupby("day").agg({"tip": np.mean, "day": np.size})

    NameError: name 'tips' is not defined

Grouping by more than one column is done by passing a list of columns to the `groupby()` method.

    SELECT smoker, day, COUNT(*), AVG(tip)
    FROM tips
    GROUP BY smoker, day;
    /*
    smoker day
    No     Fri      4  2.812500
           Sat     45  3.102889
           Sun     57  3.167895
           Thu    45  2.673778
    Yes    Fri     15  2.714000
           Sat     42  2.875476
           Sun     19  3.516842
           Thu    17  3.030000
    */

    In [23]: tips.groupby(["smoker", "day"]).agg({"tip": [np.size, np.mean]})
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-23-0117fda0b61c> in <module>
    ----> 1 tips.groupby(["smoker", "day"]).agg({"tip": [np.size, np.mean]})

    NameError: name 'tips' is not defined

## JOIN

`JOIN`s can be performed with `join()` or `merge()`. By default, `join()` will join the DataFrames on their indices. Each method has parameters allowing you to specify the type of join to perform (`LEFT`, `RIGHT`, `INNER`, `FULL`) or the columns to join on (column names or indices).

    In [24]: df1 = pd.DataFrame({"key": ["A", "B", "C", "D"], "value": np.random.randn(4)})

    In [25]: df2 = pd.DataFrame({"key": ["B", "D", "D", "E"], "value": np.random.randn(4)})

Assume we have two database tables of the same name and structure as our DataFrames.

Now let’s go over the various types of `JOIN`s.

### INNER JOIN

    SELECT *
    FROM df1
    INNER JOIN df2
      ON df1.key = df2.key;

    # merge performs an INNER JOIN by default
    In [26]: pd.merge(df1, df2, on="key")
    Out[26]:
      key   value_x   value_y
    0   B -0.282863  1.212112
    1   D -1.135632 -0.173215
    2   D -1.135632  0.119209

`merge()` also offers parameters for cases when you’d like to join one DataFrame’s column with another DataFrame’s index.

    In [27]: indexed_df2 = df2.set_index("key")

    In [28]: pd.merge(df1, indexed_df2, left_on="key", right_index=True)
    Out[28]:
      key   value_x   value_y
    1   B -0.282863  1.212112
    3   D -1.135632 -0.173215
    3   D -1.135632  0.119209

### LEFT OUTER JOIN

Show all records from `df1`.

    SELECT *
    FROM df1
    LEFT OUTER JOIN df2
      ON df1.key = df2.key;

    In [29]: pd.merge(df1, df2, on="key", how="left")
    Out[29]:
      key   value_x   value_y
    0   A  0.469112       NaN
    1   B -0.282863  1.212112
    2   C -1.509059       NaN
    3   D -1.135632 -0.173215
    4   D -1.135632  0.119209

### RIGHT JOIN

Show all records from `df2`.

    SELECT *
    FROM df1
    RIGHT OUTER JOIN df2
      ON df1.key = df2.key;

    In [30]: pd.merge(df1, df2, on="key", how="right")
    Out[30]:
      key   value_x   value_y
    0   B -0.282863  1.212112
    1   D -1.135632 -0.173215
    2   D -1.135632  0.119209
    3   E       NaN -1.044236

### FULL JOIN

pandas also allows for `FULL JOIN`s, which display both sides of the dataset, whether or not the joined columns find a match. As of writing, `FULL JOIN`s are not supported in all RDBMS (MySQL).

Show all records from both tables.

    SELECT *
    FROM df1
    FULL OUTER JOIN df2
      ON df1.key = df2.key;

    In [31]: pd.merge(df1, df2, on="key", how="outer")
    Out[31]:
      key   value_x   value_y
    0   A  0.469112       NaN
    1   B -0.282863  1.212112
    2   C -1.509059       NaN
    3   D -1.135632 -0.173215
    4   D -1.135632  0.119209
    5   E       NaN -1.044236

## UNION

`UNION ALL` can be performed using `concat()`.

    In [32]: df1 = pd.DataFrame(
       ....:     {"city": ["Chicago", "San Francisco", "New York City"], "rank": range(1, 4)}
       ....: )
       ....:

    In [33]: df2 = pd.DataFrame(
       ....:     {"city": ["Chicago", "Boston", "Los Angeles"], "rank": [1, 4, 5]}
       ....: )
       ....:

    SELECT city, rank
    FROM df1
    UNION ALL
    SELECT city, rank
    FROM df2;
    /*
             city  rank
          Chicago     1
    San Francisco     2
    New York City     3
          Chicago     1
           Boston     4
      Los Angeles     5
    */

    In [34]: pd.concat([df1, df2])
    Out[34]:
                city  rank
    0        Chicago     1
    1  San Francisco     2
    2  New York City     3
    0        Chicago     1
    1         Boston     4
    2    Los Angeles     5

SQL’s `UNION` is similar to `UNION ALL`, however `UNION` will remove duplicate rows.

    SELECT city, rank
    FROM df1
    UNION
    SELECT city, rank
    FROM df2;
    -- notice that there is only one Chicago record this time
    /*
             city  rank
          Chicago     1
    San Francisco     2
    New York City     3
           Boston     4
      Los Angeles     5
    */

In pandas, you can use `concat()` in conjunction with `drop_duplicates()`.

    In [35]: pd.concat([df1, df2]).drop_duplicates()
    Out[35]:
                city  rank
    0        Chicago     1
    1  San Francisco     2
    2  New York City     3
    1         Boston     4
    2    Los Angeles     5

## LIMIT

    SELECT * FROM tips
    LIMIT 10;

    In [36]: tips.head(10)
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-36-7419c68c4c91> in <module>
    ----> 1 tips.head(10)

    NameError: name 'tips' is not defined

## pandas equivalents for some SQL analytic and aggregate functions

### Top n rows with offset

    -- MySQL
    SELECT * FROM tips
    ORDER BY tip DESC
    LIMIT 10 OFFSET 5;

    In [37]: tips.nlargest(10 + 5, columns="tip").tail(10)
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-37-2e5ce3bb2631> in <module>
    ----> 1 tips.nlargest(10 + 5, columns="tip").tail(10)

    NameError: name 'tips' is not defined

### Top n rows per group

    -- Oracle's ROW_NUMBER() analytic function
    SELECT * FROM (
      SELECT
        t.*,
        ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rn
      FROM tips t
    )
    WHERE rn < 3
    ORDER BY day, rn;

    In [38]: (
       ....:     tips.assign(
       ....:         rn=tips.sort_values(["total_bill"], ascending=False)
       ....:         .groupby(["day"])
       ....:         .cumcount()
       ....:         + 1
       ....:     )
       ....:     .query("rn < 3")
       ....:     .sort_values(["day", "rn"])
       ....: )
       ....:
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-38-27d8ac341cab> in <module>
          1 (
    ----> 2     tips.assign(
          3         rn=tips.sort_values(["total_bill"], ascending=False)
          4         .groupby(["day"])
          5         .cumcount()

    NameError: name 'tips' is not defined

the same using `rank(method='first')` function

    In [39]: (
       ....:     tips.assign(
       ....:         rnk=tips.groupby(["day"])["total_bill"].rank(
       ....:             method="first", ascending=False
       ....:         )
       ....:     )
       ....:     .query("rnk < 3")
       ....:     .sort_values(["day", "rnk"])
       ....: )
       ....:
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-39-48f090f0aaed> in <module>
          1 (
    ----> 2     tips.assign(
          3         rnk=tips.groupby(["day"])["total_bill"].rank(
          4             method="first", ascending=False
          5         )

    NameError: name 'tips' is not defined

    -- Oracle's RANK() analytic function
    SELECT * FROM (
      SELECT
        t.*,
        RANK() OVER(PARTITION BY sex ORDER BY tip) AS rnk
      FROM tips t
      WHERE tip < 2
    )
    WHERE rnk < 3
    ORDER BY sex, rnk;

Let’s find tips with (rank < 3) per gender group for (tips < 2). Notice that when using `rank(method='min')` function `rnk_min` remains the same for the same `tip` (as Oracle’s `RANK()` function)

    In [40]: (
       ....:     tips[tips["tip"] < 2]
       ....:     .assign(rnk_min=tips.groupby(["sex"])["tip"].rank(method="min"))
       ....:     .query("rnk_min < 3")
       ....:     .sort_values(["sex", "rnk_min"])
       ....: )
       ....:
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-40-5d91b2c7a36f> in <module>
          1 (
    ----> 2     tips[tips["tip"] < 2]
          3     .assign(rnk_min=tips.groupby(["sex"])["tip"].rank(method="min"))
          4     .query("rnk_min < 3")
          5     .sort_values(["sex", "rnk_min"])

    NameError: name 'tips' is not defined

## UPDATE

    UPDATE tips
    SET tip = tip*2
    WHERE tip < 2;

    In [41]: tips.loc[tips["tip"] < 2, "tip"] *= 2
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-41-7f720871ac63> in <module>
    ----> 1 tips.loc[tips["tip"] < 2, "tip"] *= 2

    NameError: name 'tips' is not defined

## DELETE

    DELETE FROM tips
    WHERE tip > 9;

In pandas we select the rows that should remain instead of deleting them:

    In [42]: tips = tips.loc[tips["tip"] <= 9]
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-42-c23e77547afb> in <module>
    ----> 1 tips = tips.loc[tips["tip"] <= 9]

    NameError: name 'tips' is not defined