How do you select specific rows in python?
Need to select rows from Pandas DataFrame? Show
If so, you’ll see the full steps to select rows from Pandas DataFrame based on the conditions specified. Step 1: Gather your dataFirstly, you’ll need to gather your data. Here is an example of a data gathered about boxes:
Step 2: Create a DataFrameOnce you have your data ready, you’ll need to create a DataFrame to capture that data in Python. For our example, you may use the code below to create a DataFrame: import pandas as pd boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'], 'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'], 'Price': [10,15,5,5,10,15,15,5] } df = pd.DataFrame(boxes, columns= ['Color','Shape','Price']) print (df) Run the code in Python and you’ll see this DataFrame:
Step 3: Select Rows from Pandas DataFrameYou can use the following logic to select rows from Pandas DataFrame based on specified conditions: df.loc[df[‘column name’] condition] For example, if you want to get the rows where the color is green, then you’ll need to apply: df.loc[df[‘Color’] == ‘Green’] Where:
And here is the full Python code for our example: import pandas as pd boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'], 'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'], 'Price': [10,15,5,5,10,15,15,5] } df = pd.DataFrame(boxes, columns= ['Color','Shape','Price']) select_color = df.loc[df['Color'] == 'Green'] print (select_color) Once you run the code, you’ll get the rows where the color is green:
Additional Examples of Selecting Rows from Pandas DataFrameLet’s now review additional examples to get a better sense of selecting rows from Pandas DataFrame. Example 1: Select rows where the price is equal or greater than 10To get all the rows where the price is equal or greater than 10, you’ll need to apply this condition: df.loc[df[‘Price’] >= 10] And this is the complete Python code: import pandas as pd boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'], 'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'], 'Price': [10,15,5,5,10,15,15,5] } df = pd.DataFrame(boxes, columns= ['Color','Shape','Price']) select_price = df.loc[df['Price'] >= 10] print (select_price) Run the code, and you’ll get all the rows where the price is equal or greater than 10:
Example 2: Select rows where the color is green AND the shape is rectangleNow the goal is to select rows based on two conditions:
You may then use the & symbol to apply multiple conditions. In our example, the code would look like this: df.loc[(df[‘Color’] == ‘Green’) & (df[‘Shape’] == ‘Rectangle’)] Putting everything together: import pandas as pd boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'], 'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'], 'Price': [10,15,5,5,10,15,15,5] } df = pd.DataFrame(boxes, columns= ['Color','Shape','Price']) color_and_shape = df.loc[(df['Color'] == 'Green') & (df['Shape'] == 'Rectangle')] print (color_and_shape) Run the code and you’ll get the rows with the green color and rectangle shape:
Example 3: Select rows where the color is green OR the shape is rectangleYou can also select the rows based on one condition or another. For instance, you can select the rows if the color is green or the shape is rectangle. To achieve this goal, you can use the | symbol as follows: df.loc[(df[‘Color’] == ‘Green’) | (df[‘Shape’] == ‘Rectangle’)] And here is the complete Python code: import pandas as pd boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'], 'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'], 'Price': [10,15,5,5,10,15,15,5] } df = pd.DataFrame(boxes, columns= ['Color','Shape','Price']) color_or_shape = df.loc[(df['Color'] == 'Green') | (df['Shape'] == 'Rectangle')] print (color_or_shape) Here is the result, where the color is green or the shape is rectangle:
Example 4: Select rows where the price is not equal to 15You can use the combination of symbols != to select the rows where the price is not equal to 15: df.loc[df[‘Price’] != 15] import pandas as pd boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'], 'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'], 'Price': [10,15,5,5,10,15,15,5] } df = pd.DataFrame(boxes, columns= ['Color','Shape','Price']) not_eqaul_to = df.loc[df['Price'] != 15] print (not_eqaul_to) Once you run the code, you’ll get all the rows where the price is not equal to 15:
Finally, the following source provides additional information about indexing and selecting data. How do you select a group of rows in Python?To access a group of rows in a Pandas DataFrame, we can use the loc() method. For example, if we use df. loc[2:5], then it will select all the rows from 2 to 5.
How do I read a specific row in a DataFrame in Python?Steps to Select Rows from Pandas DataFrame. Step 1: Data Setup. Pandas read_csv() is an inbuilt function used to import the data from a CSV file and analyze that data in Python. ... . Step 2: Import CSV Data. ... . Step 3: Select Rows from Pandas DataFrame.. How do you filter certain rows in Python?Filter Rows by Condition
You can use df[df["Courses"] == 'Spark'] to filter rows by a condition in pandas DataFrame. Not that this expression returns a new DataFrame with selected rows. You can also write the above statement with a variable.
How do I select multiple rows in Python?Select rows by multiple conditions in Pandas. loc[] to Select mutiple rows based on column value. ... . Select Mutiple row using loc[] and & operator. ... . Select row based on Value in Column. ... . Select row based on multiple values in column. ... . Select row based on any of mutiple condition. ... . Select Mutiple Rows using OR operator.. How do I select specific rows and columns from a DataFrame?To select a single value from the DataFrame, you can do the following. You can use slicing to select a particular column. To select rows and columns simultaneously, you need to understand the use of comma in the square brackets.
How do I print specific rows and columns in Python?You can use the df. loc[[2]] to print a specific row of a pandas dataframe.
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