Python pandas drop rows

Pandas Drop Rows From DataFrame Examples

By using pandas.DataFrame.drop() method you can drop/remove/delete rows from DataFrame. axis param is used to specify what axis you would like to remove. By default axis = 0 meaning to remove rows. Use axis=1 or columns param to remove columns. By default, pandas return a copy DataFrame after deleting rows, use inpalce=True to remove from existing referring DataFrame.

In this article, I will cover how to remove rows by labels, by indexes, by ranges and how to drop inplace and None , Nan & Null values with examples. if you have duplicate rows, use drop_duplicates() to drop duplicate rows from pandas DataFrame

1. Pandas.DataFrame.drop() Syntax – Drop Rows & Columns

 # Pandas DaraFrame drop() Syntax DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise') 

• labels – Single label or list-like. It’s used with axis param.
• axis – Default set’s to 0. 1 to drop columns and 0 to drop rows.
• index – Use to specify rows. Accepts single label or list-like.
• columns – Use to specify columns. Accepts single label or list-like.
• level – int or level name, optional, use for Multiindex.
• inplace – Default False , returns a copy of DataFrame. When used True , it drop’s column inplace (current DataFrame) and returns None .
• errors – , default ‘raise’

Let’s create a DataFrame, run some examples and explore the output. Note that our DataFrame contains index labels for rows which I am going to use to demonstrate removing rows by labels.

 # Create a DataFrame import pandas as pd import numpy as np technologies = < 'Courses':["Spark","PySpark","Hadoop","Python"], 'Fee' :[20000,25000,26000,22000], 'Duration':['30day','40days',np.nan, None], 'Discount':[1000,2300,1500,1200] >indexes=['r1','r2','r3','r4'] df = pd.DataFrame(technologies,index=indexes) print(df) 

 # Output: Courses Fee Duration Discount r1 Spark 20000 30day 1000 r2 PySpark 25000 40days 2300 r3 Hadoop 26000 NaN 1500 r4 Python 22000 None 1200 

2. pandas Drop Rows From DataFrame Examples

By default drop() method removes rows ( axis=0 ) from DataFrame. Let’s see several examples of how to remove rows from DataFrame.

2.1 Drop rows by Index Labels or Names

One of the pandas advantages is you can assign labels/names to rows, similar to column names. If you have DataFrame with row labels (index labels), you can specify what rows you wanted to remove by label names.

 # Drop rows by Index Label df = pd.DataFrame(technologies,index=indexes) df1 = df.drop(['r1','r2']) print(df1) 

 # Output: Courses Fee Duration Discount r3 Hadoop 26000 NaN 1500 r4 Python 22000 None 1200 

Alternatively, you can also write the same statement by using the field name ‘index’ .

 # Delete Rows by Index Labels df1 = df.drop(index=['r1','r2']) 

And by using labels and axis as below.

 # Delete Rows by Index Labels & axis df1 = df.drop(labels=['r1','r2']) df1 = df.drop(labels=['r1','r2'],axis=0) 

• As you see using labels, axis=0 is equivalent to using index=label names .
• axis=0 mean rows. By default drop() method considers axis=0 hence you don’t have to specify to remove rows. to remove columns explicitly specify axis=1 or columns .

2.2 Drop Rows by Index Number (Row Number)

Similarly by using drop() method you can also remove rows by index position from pandas DataFrame. drop() method doesn’t have position index as a param, hence we need to get the row labels from the index and pass these to the drop method. We will use df.index to get us row labels for the indexes we wanted to delete.

• df.index.values returns all row labels as list.
• df.index[[1,3]] get’s you row labels for 2nd and 3rd rows, by passing these to drop() method removes these rows. Note that in python list index starts from zero.

 # Delete Rows by Index numbers df = pd.DataFrame(technologies,index=indexes) df1=df.drop(df.index[[1,3]]) print(df1) 

Yields the same output as section 2.1. In order to remove the first row, you can use df.drop(df.index[0]) , and to remove the last row use df.drop(df.index[-1]) .

 # Removes First Row df=df.drop(df.index[0]) # Removes Last Row df=df.drop(df.index[-1]) 

2.3 Delete Rows by Index Range

You can also remove rows by specifying the index range. The below example removes all rows starting 3rd row.

 # Delete Rows by Index Range df = pd.DataFrame(technologies,index=indexes) df1=df.drop(df.index[2:]) print(df1) 

 # Output: Courses Fee Duration Discount r1 Spark 20000 30day 1000 r2 PySpark 25000 40days 2300 

2.4 Delete Rows when you have Default Indexs

By default pandas assign a sequence number to all rows also called index, row index starts from zero and increments by 1 for every row. If you are not using custom index labels then pandas DataFrame assigns sequence numbers as Index. To remove rows with the default index, you can try below.

 # Remove rows when you have default index. df = pd.DataFrame(technologies) df1 = df.drop(0) df3 = df.drop([0, 3]) df4 = df.drop(range(0,2)) 

Note that df.drop(-1) doesn’t remove the last row as -1 index not present in DataFrame. You can still use df.drop(df.index[-1]) to remove the last row.

2.5 Remove DataFrame Rows inplace

All examples you have seen above return a copy DataFrame after removing rows. In case if you wanted to remove rows inplace from referring DataFrame use inplace=True . By default inplace param is set to False .

 # Delete Rows inplace df = pd.DataFrame(technologies,index=indexes) df.drop(['r1','r2'],inplace=True) print(df) 

2.6 Drop Rows by Checking Conditions

Most of the time we would also need to remove DataFrame rows based on some conditions (column value), you can do this by using loc[] and iloc[] methods.

 # Delete Rows by Checking Conditions df = pd.DataFrame(technologies) df1 = df.loc[df["Discount"] >=1500 ] print(df1) 

 # Output: Courses Fee Duration Discount 1 PySpark 25000 40days 2300 2 Hadoop 26000 NaN 1500 

2.7 Drop Rows that has NaN/None/Null Values

While working with analytics you would often be required to clean up the data that has None , Null & np.NaN values. By using df.dropna() you can remove NaN values from DataFrame.

 # Delete rows with Nan, None & Null Values df = pd.DataFrame(technologies,index=indexes) df2=df.dropna() print(df2) 

This removes all rows that have None, Null & NaN values on any columns.

 # Output: Courses Fee Duration Discount r1 Spark 20000 30day 1000 r2 PySpark 25000 40days 2300 

2.8 Remove Rows by Slicing DataFrame

You can also remove DataFrame rows by slicing. Remember index starts from zero.

 # Remove Rows by Slicing DataFrame df2=df[4:] # Returns rows from 4th row df2=df[1:-1] # Removes first and last row df2=df[2:4] # Return rows between 2 and 4 

Conclusion

In this pandas drop rows article you have learned how to drop/remove pandas DataFrame rows using drop() method. By default drop() deletes rows (axis = 0), if you wanted to delete columns either you have to use axis =1 or columns=labels param.

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References

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pandas.DataFrame.drop#

Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide for more information about the now unused levels.

Parameters labels single label or list-like

Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like.

Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).

index single label or list-like

Alternative to specifying axis ( labels, axis=0 is equivalent to index=labels ).

columns single label or list-like

Alternative to specifying axis ( labels, axis=1 is equivalent to columns=labels ).

level int or level name, optional

For MultiIndex, level from which the labels will be removed.

inplace bool, default False

If False, return a copy. Otherwise, do operation inplace and return None.

errors , default ‘raise’

If ‘ignore’, suppress error and only existing labels are dropped.

Returns DataFrame or None

DataFrame without the removed index or column labels or None if inplace=True .

If any of the labels is not found in the selected axis.

Label-location based indexer for selection by label.

Return DataFrame with labels on given axis omitted where (all or any) data are missing.

Return DataFrame with duplicate rows removed, optionally only considering certain columns.

Return Series with specified index labels removed.

>>> df = pd.DataFrame(np.arange(12).reshape(3, 4), . columns=['A', 'B', 'C', 'D']) >>> df A B C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 

>>> df.drop(['B', 'C'], axis=1) A D 0 0 3 1 4 7 2 8 11 

>>> df.drop(columns=['B', 'C']) A D 0 0 3 1 4 7 2 8 11 

>>> df.drop([0, 1]) A B C D 2 8 9 10 11 

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'], . ['speed', 'weight', 'length']], . codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], . [0, 1, 2, 0, 1, 2, 0, 1, 2]]) >>> df = pd.DataFrame(index=midx, columns=['big', 'small'], . data=[[45, 30], [200, 100], [1.5, 1], [30, 20], . [250, 150], [1.5, 0.8], [320, 250], . [1, 0.8], [0.3, 0.2]]) >>> df big small lama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 weight 1.0 0.8 length 0.3 0.2 

Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination ‘falcon’ and ‘weight’ , which deletes only the corresponding row

>>> df.drop(index=('falcon', 'weight')) big small lama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 length 0.3 0.2 

>>> df.drop(index='cow', columns='small') big lama speed 45.0 weight 200.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3 

>>> df.drop(index='length', level=1) big small lama speed 45.0 30.0 weight 200.0 100.0 cow speed 30.0 20.0 weight 250.0 150.0 falcon speed 320.0 250.0 weight 1.0 0.8 

Источник

pandas.DataFrame.drop#

Remove rows or columns by specifying label names and corresponding axis, or by directly specifying index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide for more information about the now unused levels.

Parameters : labels single label or list-like

Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like.

Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).

index single label or list-like

Alternative to specifying axis ( labels, axis=0 is equivalent to index=labels ).

columns single label or list-like

Alternative to specifying axis ( labels, axis=1 is equivalent to columns=labels ).

level int or level name, optional

For MultiIndex, level from which the labels will be removed.

inplace bool, default False

If False, return a copy. Otherwise, do operation in place and return None.

errors , default ‘raise’

If ‘ignore’, suppress error and only existing labels are dropped.

Returns : DataFrame or None

DataFrame without the removed index or column labels or None if inplace=True .

If any of the labels is not found in the selected axis.

Label-location based indexer for selection by label.

Return DataFrame with labels on given axis omitted where (all or any) data are missing.

Return DataFrame with duplicate rows removed, optionally only considering certain columns.

Return Series with specified index labels removed.

>>> df = pd.DataFrame(np.arange(12).reshape(3, 4), . columns=['A', 'B', 'C', 'D']) >>> df A B C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 

>>> df.drop(['B', 'C'], axis=1) A D 0 0 3 1 4 7 2 8 11 

>>> df.drop(columns=['B', 'C']) A D 0 0 3 1 4 7 2 8 11 

>>> df.drop([0, 1]) A B C D 2 8 9 10 11 

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'], . ['speed', 'weight', 'length']], . codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], . [0, 1, 2, 0, 1, 2, 0, 1, 2]]) >>> df = pd.DataFrame(index=midx, columns=['big', 'small'], . data=[[45, 30], [200, 100], [1.5, 1], [30, 20], . [250, 150], [1.5, 0.8], [320, 250], . [1, 0.8], [0.3, 0.2]]) >>> df big small llama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 weight 1.0 0.8 length 0.3 0.2 

Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination ‘falcon’ and ‘weight’ , which deletes only the corresponding row

>>> df.drop(index=('falcon', 'weight')) big small llama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 length 0.3 0.2 

>>> df.drop(index='cow', columns='small') big llama speed 45.0 weight 200.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3 

>>> df.drop(index='length', level=1) big small llama speed 45.0 30.0 weight 200.0 100.0 cow speed 30.0 20.0 weight 250.0 150.0 falcon speed 320.0 250.0 weight 1.0 0.8 

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