To delete every 5 rows in a pandas DataFrame, you can use the drop method with a custom function that filters out every 5th row.
Here is an example code snippet that demonstrates this:
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import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'B': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']} df = pd.DataFrame(data) # Define a custom function to filter out every 5th row def keep_every_5th_row(index): return index % 5 != 0 # Use the custom function with the drop method df = df[df.index.to_series().apply(keep_every_5th_row)] print(df) |
In this example, we create a custom function called keep_every_5th_row that returns True for rows that are not multiples of 5. We then use the apply method to apply this function to the DataFrame's index and filter out every 5th row using the drop method.
What is the outcome of deleting rows from pandas dataframe in terms of memory consumption?
Deleting rows from a pandas dataframe reduces the memory consumption of the dataframe, as it removes the data associated with the deleted rows from memory. This can be especially useful when working with large datasets, as it frees up memory for other operations and can improve overall performance.
How to delete rows from pandas dataframe based on index?
You can delete rows from a Pandas dataframe based on index using the drop() method. Here's an example of how to do this:
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import pandas as pd # create a sample dataframe data = {'A': [1, 2, 3, 4], 'B': ['a', 'b', 'c', 'd']} df = pd.DataFrame(data) # display the dataframe print("Original DataFrame:") print(df) # delete rows with index 1 and 3 df = df.drop([1, 3]) # display the updated dataframe print("\nUpdated DataFrame:") print(df) |
In the code above, we first create a sample dataframe, display it, and then use the drop() method to delete rows with index 1 and 3. The updated dataframe is then displayed.
How to skip every 5 rows in pandas dataframe?
To skip every 5 rows in a pandas dataframe, you can use the iloc method along with slicing. Here is an example:
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import pandas as pd # Create a sample dataframe data = {'A': range(20), 'B': range(20)} df = pd.DataFrame(data) # Skip every 5th row df_filtered = df.iloc[~(df.index % 5).astype(bool)] print(df_filtered) |
In this example, we are skipping every 5th row by using the modulo operator % to check if the row number is divisible by 5. The ~ operator is used to invert the boolean mask, so we select all the rows that are not divisible by 5.
How to drop every 5th row in pandas dataframe?
You can drop every 5th row in a pandas dataframe by creating a boolean mask and using it to filter out the rows that you want to drop. Here is an example code snippet to achieve this:
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import pandas as pd # Create a sample dataframe data = {'A': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'B': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']} df = pd.DataFrame(data) # Create a boolean mask to identify every 5th row mask = df.index % 5 == 0 # Drop every 5th row from the dataframe df = df[~mask] print(df) |
In this code snippet, we first create a boolean mask using df.index % 5 == 0 which checks if the index of each row is divisible by 5. We then use this mask to filter out the rows that are divisible by 5 using df[~mask]. The ~ symbol inverts the boolean values in the mask, so that we get all rows except the ones that we want to drop.
