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I want to drop m number of rows from the bottom of a data frame. It is integer indexed (with holes). How can this be done?
pandas == 0.10.1 python == 2.7.3
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Using iloc[] to Drop First N Rows of DataFrameUse DataFrame. iloc[] the indexing syntax [n:] with n as an integer to select the first n rows from pandas DataFrame. For example df. iloc[n:] , substitute n with the integer number specifying how many rows you wanted to delete.
Delete a Multiple Rows by Index Position in DataFrame As df. drop() function accepts only list of index label names only, so to delete the rows by position we need to create a list of index names from positions and then pass it to drop(). As default value of inPlace is false, so contents of dfObj will not be modified.
To delete rows and columns from DataFrames, Pandas uses the “drop” function. To delete a column, or multiple columns, use the name of the column(s), and specify the “axis” as 1. Alternatively, as in the example below, the 'columns' parameter has been added in Pandas which cuts out the need for 'axis'.
Use slice to select the part you want:
df[:-m] If you want to remove some middle rows, you can use drop:
df.drop(df.index[3:5]) an example where the range you want to drop is indexes between x and y which I have set to 0 and 10
selecting just the locations between 0 and 10 to see the rows to confirm before removing
x=0 # could change x and y to a start and end date y=10 df.loc[x:y] selecting the index
df.loc[x:y].index so to remove selection from dataframe
df.drop(df.loc[x:y].index, inplace=True) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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