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# 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/pydata/pandas/master/pandas/tests/data/tips.csv'

    In [4]: tips = pd.read_csv(url)

    In [5]: tips.head()
    Out[5]:
       total_bill   tip     sex smoker  day    time  size
    0       16.99  1.01  Female     No  Sun  Dinner     2
    1       10.34  1.66    Male     No  Sun  Dinner     3
    2       21.01  3.50    Male     No  Sun  Dinner     3
    3       23.68  3.31    Male     No  Sun  Dinner     2
    4       24.59  3.61  Female     No  Sun  Dinner     4

    [5 rows x 7 columns]

## 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
    LIMIT 5;

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

    In [6]: tips[['total_bill', 'tip', 'smoker', 'time']].head(5)
    Out[6]:
       total_bill   tip smoker    time
    0       16.99  1.01     No  Dinner
    1       10.34  1.66     No  Dinner
    2       21.01  3.50     No  Dinner
    3       23.68  3.31     No  Dinner
    4       24.59  3.61     No  Dinner

    [5 rows x 4 columns]

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

## WHERE

Filtering in SQL is done via a WHERE clause.

    SELECT *
    FROM tips
    WHERE time = 'Dinner'
    LIMIT 5;

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

    In [7]: tips[tips['time'] == 'Dinner'].head(5)
    Out[7]:
       total_bill   tip     sex smoker  day    time  size
    0       16.99  1.01  Female     No  Sun  Dinner     2
    1       10.34  1.66    Male     No  Sun  Dinner     3
    2       21.01  3.50    Male     No  Sun  Dinner     3
    3       23.68  3.31    Male     No  Sun  Dinner     2
    4       24.59  3.61  Female     No  Sun  Dinner     4

    [5 rows x 7 columns]

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

    In [8]: is_dinner = tips['time'] == 'Dinner'

    In [9]: is_dinner.value_counts()
    Out[9]:
    True     176
    False     68
    dtype: int64

    In [10]: tips[is_dinner].head(5)
    Out[10]:
       total_bill   tip     sex smoker  day    time  size
    0       16.99  1.01  Female     No  Sun  Dinner     2
    1       10.34  1.66    Male     No  Sun  Dinner     3
    2       21.01  3.50    Male     No  Sun  Dinner     3
    3       23.68  3.31    Male     No  Sun  Dinner     2
    4       24.59  3.61  Female     No  Sun  Dinner     4

    [5 rows x 7 columns]

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

    -- tips of more than $5.00 at Dinner meals
    SELECT *
    FROM tips
    WHERE time = 'Dinner' AND tip > 5.00;

    # tips of more than $5.00 at Dinner meals
    In [11]: tips[(tips['time'] == 'Dinner') & (tips['tip'] > 5.00)]
    Out[11]:
         total_bill    tip     sex smoker  day    time  size
    23        39.42   7.58    Male     No  Sat  Dinner     4
    44        30.40   5.60    Male     No  Sun  Dinner     4
    47        32.40   6.00    Male     No  Sun  Dinner     4
    52        34.81   5.20  Female     No  Sun  Dinner     4
    59        48.27   6.73    Male     No  Sat  Dinner     4
    116       29.93   5.07    Male     No  Sun  Dinner     4
    155       29.85   5.14  Female     No  Sun  Dinner     5
    170       50.81  10.00    Male    Yes  Sat  Dinner     3
    172        7.25   5.15    Male    Yes  Sun  Dinner     2
    181       23.33   5.65    Male    Yes  Sun  Dinner     2
    183       23.17   6.50    Male    Yes  Sun  Dinner     4
    211       25.89   5.16    Male    Yes  Sat  Dinner     4
    212       48.33   9.00    Male     No  Sat  Dinner     4
    214       28.17   6.50  Female    Yes  Sat  Dinner     3
    239       29.03   5.92    Male     No  Sat  Dinner     3

    [15 rows x 7 columns]

    -- 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;

    # tips by parties of at least 5 diners OR bill total was more than $45
    In [12]: tips[(tips['size'] >= 5) | (tips['total_bill'] > 45)]
    Out[12]:
         total_bill    tip     sex smoker   day    time  size
    59        48.27   6.73    Male     No   Sat  Dinner     4
    125       29.80   4.20  Female     No  Thur   Lunch     6
    141       34.30   6.70    Male     No  Thur   Lunch     6
    142       41.19   5.00    Male     No  Thur   Lunch     5
    143       27.05   5.00  Female     No  Thur   Lunch     6
    155       29.85   5.14  Female     No   Sun  Dinner     5
    156       48.17   5.00    Male     No   Sun  Dinner     6
    170       50.81  10.00    Male    Yes   Sat  Dinner     3
    182       45.35   3.50    Male    Yes   Sun  Dinner     3
    185       20.69   5.00    Male     No   Sun  Dinner     5
    187       30.46   2.00    Male    Yes   Sun  Dinner     5
    212       48.33   9.00    Male     No   Sat  Dinner     4
    216       28.15   3.00    Male    Yes   Sat  Dinner     5

    [13 rows x 7 columns]

NULL checking is done using the `notnull()` and `isnull()` methods.

    In [13]: frame = pd.DataFrame({'col1': ['A', 'B', np.NaN, 'C', 'D'],
       ....:                       'col2': ['F', np.NaN, 'G', 'H', 'I']})
       ....:

    In [14]: frame
    Out[14]:
      col1 col2
    0    A    F
    1    B  NaN
    2  NaN    G
    3    C    H
    4    D    I

    [5 rows x 2 columns]

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 [15]: frame[frame['col2'].isnull()]
    Out[15]:
      col1 col2
    1    B  NaN

    [1 rows x 2 columns]

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

    SELECT *
    FROM frame
    WHERE col1 IS NOT NULL;

    In [16]: frame[frame['col1'].notnull()]
    Out[16]:
      col1 col2
    0    A    F
    1    B  NaN
    3    C    H
    4    D    I

    [4 rows x 2 columns]

## GROUP BY

In pandas, SQL’s GROUP BY operations 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 [17]: tips.groupby('sex').size()
    Out[17]:
    sex
    Female     87
    Male      157
    dtype: int64

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 [18]: tips.groupby('sex').count()
    Out[18]:
            total_bill  tip  sex  smoker  day  time  size
    sex
    Female          87   87   87      87   87    87    87
    Male           157  157  157     157  157   157   157

    [2 rows x 7 columns]

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

    In [19]: tips.groupby('sex')['total_bill'].count()
    Out[19]:
    sex
    Female     87
    Male      157
    dtype: int64

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
    Thur  2.771452   62
    */

    In [20]: tips.groupby('day').agg({'tip': np.mean, 'day': np.size})
    Out[20]:
               tip  day
    day
    Fri   2.734737   19
    Sat   2.993103   87
    Sun   3.255132   76
    Thur  2.771452   62

    [4 rows x 2 columns]

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 tip
    GROUP BY smoker, day;
    /*
    smoker day
    No     Fri      4  2.812500
           Sat     45  3.102889
           Sun     57  3.167895
           Thur    45  2.673778
    Yes    Fri     15  2.714000
           Sat     42  2.875476
           Sun     19  3.516842
           Thur    17  3.030000
    */

    In [21]: tips.groupby(['smoker', 'day']).agg({'tip': [np.size, np.mean]})
    Out[21]:
                  tip
                 size      mean
    smoker day
    No     Fri      4  2.812500
           Sat     45  3.102889
           Sun     57  3.167895
           Thur    45  2.673778
    Yes    Fri     15  2.714000
           Sat     42  2.875476
           Sun     19  3.516842
           Thur    17  3.030000

    [8 rows x 2 columns]

## 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 [22]: df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
       ....:                     'value': np.random.randn(4)})
       ....:

    In [23]: 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 [24]: pd.merge(df1, df2, on='key')
    Out[24]:
      key   value_x   value_y
    0   B  0.163921  0.156585
    1   D  0.872202  0.711517
    2   D  0.872202 -0.105817

    [3 rows x 3 columns]

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

    In [25]: indexed_df2 = df2.set_index('key')

    In [26]: pd.merge(df1, indexed_df2, left_on='key', right_index=True)
    Out[26]:
      key   value_x   value_y
    1   B  0.163921  0.156585
    3   D  0.872202  0.711517
    3   D  0.872202 -0.105817

    [3 rows x 3 columns]

### LEFT OUTER JOIN

    -- show all records from df1
    SELECT *
    FROM df1
    LEFT OUTER JOIN df2
      ON df1.key = df2.key;

    # show all records from df1
    In [27]: pd.merge(df1, df2, on='key', how='left')
    Out[27]:
      key   value_x   value_y
    0   A -1.348332       NaN
    1   B  0.163921  0.156585
    2   C  0.706914       NaN
    3   D  0.872202  0.711517
    4   D  0.872202 -0.105817

    [5 rows x 3 columns]

### RIGHT JOIN

    -- show all records from df2
    SELECT *
    FROM df1
    RIGHT OUTER JOIN df2
      ON df1.key = df2.key;

    # show all records from df2
    In [28]: pd.merge(df1, df2, on='key', how='right')
    Out[28]:
      key   value_x   value_y
    0   B  0.163921  0.156585
    1   D  0.872202  0.711517
    2   D  0.872202 -0.105817
    3   E       NaN -0.087338

    [4 rows x 3 columns]

### 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;

    # show all records from both frames
    In [29]: pd.merge(df1, df2, on='key', how='outer')
    Out[29]:
      key   value_x   value_y
    0   A -1.348332       NaN
    1   B  0.163921  0.156585
    2   C  0.706914       NaN
    3   D  0.872202  0.711517
    4   D  0.872202 -0.105817
    5   E       NaN -0.087338

    [6 rows x 3 columns]

## UNION

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

    In [30]: df1 = pd.DataFrame({'city': ['Chicago', 'San Francisco', 'New York City'],
       ....:                     'rank': range(1, 4)})
       ....:

    In [31]: 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 [32]: pd.concat([df1, df2])
    Out[32]:
                city  rank
    0        Chicago     1
    1  San Francisco     2
    2  New York City     3
    0        Chicago     1
    1         Boston     4
    2    Los Angeles     5

    [6 rows x 2 columns]

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 [33]: pd.concat([df1, df2]).drop_duplicates()
    Out[33]:
                city  rank
    0        Chicago     1
    1  San Francisco     2
    2  New York City     3
    1         Boston     4
    2    Los Angeles     5

    [5 rows x 2 columns]

## UPDATE

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