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Columns and rows in Pandas DataFrame have a lot to discuss. Today, this article will show you how to use Pandas Loc[] multiple conditions to choose rows.
What Is Pandas Loc[]?
Loc is a type of command based on labels. What does that mean? It means there is the need to specify the label of every column and row you have to filter.
Suppose you want to find every row with the index of 100, 1, or 2. If that is the case, you won’t get the hundredth, first, or second row. Rather, the results only come if any index has the name of 100, 1, or 2.
As a result, the loc[] function can easily filter our data, even when the dataset does not show the indices as an integer.
How Does Loc Differ from Iloc in Pandas DataFrame?
Iloc and loc are among the most popular functions in Pandas DataFrame, especially if you need to select columns and rows. But of course, these two are not identical. Here is the primary distinction between them:
- loc chooses columns and rows via specific labels
- iloc chooses columns and rows via the positions of a specific integer.
In short, iloc is a type of integer that is based on indexes. As a result, you must use its integer index to specify columns and rows.
Supposedly you need to find rows with the index 100, 1, or 2. What you receive will be the hundredth, first, and second rows, no matter the labels or names they have in the dataset’s index.
How to Use Pandas Loc[] Multiple Conditions
Case 1: Choose Rows That FulFill Multiple Conditions
Command 1:
df.loc[((df['col1'] == 'A') & (df['col2'] == 'G'))]
Example 1:
#select rows where team is equal to 'A' and position is equal to 'G'
df.loc[((df['team'] == 'A') & (df['position'] == 'G'))]
team position assists rebounds
0 A G 5 11
1 A G 7 8
Case 2: Choose Rows That Satisfy One Condition Among Multiple
Command 2:
df.loc[((df['col1'] > 10) | (df['col2'] < 8))]
Example 2:
#select rows where assists is greater than 10 or rebounds is less than 8
df.loc[((df['assists'] > 10) | (df['rebounds'] < 8))]
team position assists rebounds
3 A F 9 6
4 B G 12 6
5 B G 9 5
FAQs
1. How Can I Use Loc to Subset?
Using Loc[] methods to receive a column subset is no different from doing the same with rows. What you need to do is call Loc first. Next, within the brackets, you will specify the labels of each column and row.
Since we need to receive all the rows, the colon (:) is required to specify every row label.
2. How Can I Use Loc to Change A DataFrame Value?
Loc will help you change a DataFrame column value depending on its conditions.
The steps to do are call Pandas.DataFrame.loc {condition, columnlabel) = newvalue to change the column value in every column with the name “columnname”. Change this value into another value for which the condition in every row returns “True”.
3. Is it Possible to Fuse Two Different DataFrames?
Of course, our answer is a Yes. Joining different DataFrames is no different from joining SQL tables. To be more specific, our concat() function is the key here. You can use it to fuse two DataFrames, adding rows of one DataFrame to another.
You can also perform a column combination via concat (). Still, in cases of columns, the merge() command might be much better and quicker.
Conclusion
Our article has shown you how to use Pandas loc[] multiple conditions to choose the rows you want. The methods are straightforward, accompanied by clear examples. Hopefully, you can find these guidelines practical.On extra methods to filter rows in Pandas, refer to this article for more instructions.
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