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I have a data frame. Then I have a logical condition using which I create another data frame by removing some rows. The new data frame however skips indices for removed rows. How can I get it to reindex sequentially without skipping? Here's a sample coded to clarify
import pandas as pd import numpy as np jjarray = np.array(range(5)) eq2 = jjarray == 2 neq2 = np.logical_not(eq2) jjdf = pd.DataFrame(jjarray) jjdfno2 = jjdf[neq2] jjdfno2 Out:
0 0 0 1 1 3 3 4 4 I want it to look like this:
0 0 0 1 1 2 3 3 4 Thanks.
535
The reindex() method allows you to change the row indexes, and the columns labels. ;] Note: The values are set to NaN if the new index is not the same as the old.
One can reindex a single column or multiple columns by using reindex() method and by specifying the axis we want to reindex. Default values in the new index that are not present in the dataframe are assigned NaN. Example #1: Python3.
Reindexing changes the row labels and column labels of a DataFrame. To reindex means to conform the data to match a given set of labels along a particular axis. Multiple operations can be accomplished through indexing like − Reorder the existing data to match a new set of labels.
REINDEX rebuilds an index using the data stored in the index's table, replacing the old copy of the index. There are several scenarios in which to use REINDEX : An index has become corrupted, and no longer contains valid data.
One way is to use reset_index:
>>> df = pd.DataFrame(range(5)) >>> eq2 = df[0] == 2 >>> df_no_2 = df[~eq2] >>> df_no_2 0 0 0 1 1 3 3 4 4 >>> df_no_2.reset_index(drop=True) 0 0 0 1 1 2 3 3 4 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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