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

JOINs 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 JOINs.

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 JOINs, which display both sides of the dataset, whether or not the joined columns find a match. As of writing, FULL JOINs 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