Select From a Table
To select from a table in MySQL, use the "SELECT" statement:
Example
Select all records from the "customers" table, and display the result:
import mysql.connector
mydb = mysql.connector.connect[
host="localhost",
user="yourusername",
password="yourpassword",
database="mydatabase"
]
mycursor = mydb.cursor[]
mycursor.execute["SELECT * FROM customers"]
myresult = mycursor.fetchall[]
for x in myresult:
print[x]
Run example »
Note: We use the fetchall[]
method, which fetches all rows from the last executed statement.
Selecting Columns
To select only some of the columns in a table, use the "SELECT" statement followed by the column name[s]:
Example
Select only the name and address columns:
import mysql.connector
mydb = mysql.connector.connect[
host="localhost",
user="yourusername",
password="yourpassword",
database="mydatabase"
]
mycursor = mydb.cursor[]
mycursor.execute["SELECT name, address FROM customers"]
myresult = mycursor.fetchall[]
for x in myresult:
print[x]
Run example »
Using the fetchone[] Method
If you are only interested in one row, you can use the fetchone[]
method.
The fetchone[]
method will return the first row of the result:
Example
Fetch only one row:
import mysql.connector
mydb = mysql.connector.connect[
host="localhost",
user="yourusername",
password="yourpassword",
database="mydatabase"
]
mycursor = mydb.cursor[]
mycursor.execute["SELECT * FROM customers"]
myresult = mycursor.fetchone[]
print[myresult]
Run example »
Query the columns of a DataFrame with a boolean expression. The query string to evaluate. You can refer to variables in the environment by prefixing them with an ‘@’ character like You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations [besides underscores] or starting with digits must be surrounded by backticks. [For example, a column named “Area [cm^2]” would be referenced as
For example, if one of your columns is called New in version 0.25.0: Backtick quoting introduced. New in version 1.0.0: Expanding functionality of backtick quoting for more than only spaces. Whether to modify
the DataFrame rather than creating a new one. See the documentation for DataFrame resulting from the provided query expression or None if See also Evaluate a string describing operations on DataFrame columns. Evaluate a string describing operations on DataFrame columns. Notes The result of the evaluation of this expression is first passed to This method uses the top-level The @a + b
.`Area [cm^2]`
]. Column names which are Python keywords [like “list”, “for”, “import”, etc] cannot be used.a a
and you want to sum it with b
, your query should be `a a` + b
.eval[]
for complete details on the keyword arguments accepted by DataFrame.query[]
.inplace=True
.eval
DataFrame.eval
DataFrame.loc
and if
that fails because of a multidimensional key [e.g., a DataFrame] then the result will be passed to DataFrame.__getitem__[]
.eval[]
function to evaluate the passed query.query[]
method
uses a slightly modified Python syntax by default. For example, the &
and |
[bitwise] operators have the precedence of their boolean cousins, and
and or
. This is syntactically valid Python, however the semantics are different.
You can
change the semantics of the expression by passing the keyword argument parser='python'
. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python'
to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr
as the engine.
The DataFrame.index
and DataFrame.columns
attributes of the DataFrame
instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The
identifier index
is used for the frame index; you can also use the name of the index to identify it in a query. Please note that Python keywords may not be used as identifiers.
For further details and examples see the query
documentation in indexing.
Backtick quoted variables
Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.
During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range [U+0001..U+007F] and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag [as it is used for comments] and the backtick itself [backtick can also not be escaped].
In a special case, quotes that make a pair around a backtick can confuse the parser. For example, `it's` > `that's`
will raise an error, as it forms a quoted string ['s > `that'
] with a backtick inside.
See also the Python documentation about lexical analysis
[//docs.python.org/3/reference/lexical_analysis.html] in combination with the source code in pandas.core.computation.parsing
.
Examples
>>> df = pd.DataFrame[{'A': range[1, 6], ... 'B': range[10, 0, -2], ... 'C C': range[10, 5, -1]}] >>> df A B C C 0 1 10 10 1 2 8 9 2 3 6 8 3 4 4 7 4 5 2 6 >>> df.query['A > B'] A B C C 4 5 2 6
The previous expression is equivalent to
>>> df[df.A > df.B] A B C C 4 5 2 6
For columns with spaces in their name, you can use backtick quoting.
>>> df.query['B == `C C`'] A B C C 0 1 10 10
The previous expression is equivalent to
>>> df[df.B == df['C C']] A B C C 0 1 10 10