All Answers Tagged With pandas
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Easy
Topics
Companies
SQL Schema
Pandas Schema
Table: Sales
+-------------+-------+
| Column Name | Type |
+-------------+-------+
| sale_id | int |
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Topics
Companies
SQL Schema
Pandas Schema
Table: Weather
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
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| temperature | int |
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Companies
SQL Schema
Pandas Schema
Table: Activity
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| machine_id | int |
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| ac
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Topics
Companies
Hint
SQL Schema
Pandas Schema
Table: Employee
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| empId | int |
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Easy
Topics
Companies
SQL Schema
Pandas Schema
Table: Cinema
+----------------+----------+
| Column Name | Type |
+----------------+----------+
| id | int |
| movie | varchar |
| description | v
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Easy
Topics
Companies
SQL Schema
Pandas Schema
Table: Users
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| user_id | int |
| user_name | varchar |
+-------------+----
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import re
def remove_km_from_mileage(mileage):
"""Removes the "km" from the mileage string and returns the numeric part."""
# Split the mileage string into two parts at the "km" character.
parts = re.split(r'km', milea
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