Use iloc[]
, drop()
and tail()
methods to drop the top/first n rows from the pandas DataFrame. In this article, I will explain how to drop/delete the first n rows from Pandas DataFrame with examples.
Related: You can also drop the last N rows from DataFrame.
1. Quick Examples of Drop First N Rows From Pandas DataFrame
If you are in a hurry, below are some quick examples of how to drop the first n rows from Pandas DataFrame.
# Below are the quick examples
# Number of rows to drop
n = 2
# By using DataFrame.iloc[] to drop first n rows
df2 = df.iloc[n:,:]
# Using iloc[] to drop first n rows
df2 = df.iloc[n:]
# Using drop() function to delete first n rows
df.drop(index=df.index[:n], axis=0, inplace=True)
# Using DataFrame.tail() to Drop top two rows
df2 = df.tail(df.shape[0] -n)
# Using DataFrame.tail() function to drop first n rows
df2 = df.tail(-2)
Now, let’s create a DataFrame with a few rows and columns, execute these examples, and validate the results. Our DataFrame contains column names Courses
, Fee
, Duration
, and Discount
.
# Create DataFrame
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Python","pandas"],
'Fee' :[20000,25000,22000,24000],
'Duration':['30days','40days','35days','60days'],
'Discount':[1000,2300,2500,2000]
}
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print("DataFrame:\n", df)
Yields below output.

2. Using iloc[] to Drop First N Rows of DataFrame
Use 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 want to delete.
# By using DataFrame.iloc[] to drop first n rows
n = 2
df2 = df.iloc[n:]
print(df2)
# Using iloc[] to drop first n rows
df2 = df.iloc[2:]
print("After dropping first n rows:\n", df2)
Yields below output.

3. Delete the Top N Rows of DataFrame Using drop()
drop()
method is also used to delete rows from DataFrame based on column values (condition).- Use
axis
param to specify what axis you would like to delete. By default axis = 0 meaning to delete rows. Useaxis=1
orcolumns
param to delete columns. - Use
inplace=True
to delete row/column in place meaning on existing DataFrame without creating copy.
# Using drop() function to delete first n rows
n = 2
df.drop(index=df.index[:n], inplace=True)
print(df)
Yields the same output as above.
4. Remove First N Rows of Pandas DataFrame Using tail()
Alternatively, you can also use df.tail(df.shape[0] -n)
to remove the top/first n rows of pandas DataFrame. Generally, the DataFrame.tail() function is used to show the last n rows of a pandas DataFrame but you can pass a negative value to skip the rows from the beginning.
# Number of rows to drop
n = 2
# Using DataFrame.tail() to Drop top two rows
df2 = df.tail(df.shape[0] -n)
print(df2)
# Using DataFrame.tail() function to drop first n rows
df2 = df.tail(-2)
print(df2)
5. Complete Example For Drop First N Rows From DataFrame
Yields the same output as above.
Below is a complete example of Dropping Top N Rows from Pandas DataFrame.
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Python","pandas"],
'Fee' :[20000,25000,22000,24000],
'Duration':['30days','40days','35days','60days'],
'Discount':[1000,2300,2500,2000]
}
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)
# Number of rows to drop
n = 2
# By using DataFrame.iloc[] to drop first n rows
df2 = df.iloc[n:,:]
print(df2)
# Using iloc[] to drop first n rows
df2 = df.iloc[2:]
print(df2)
# Number of rows to drop
n = 2
# Using drop() function to delete first n rows
df.drop(index=df.index[:n],axis=0, inplace=True)
print(df)
# Number of rows to drop
n = 2
# Using DataFrame.tail() to Drop top two rows
df2 = df.tail(df.shape[0] -n)
print(df2)
# Using DataFrame.tail() function to drop first n rows
df2 = df.tail(-2)
print(df2)
Conclusion
In this article, you have learned how to drop the first n rows From Pandas DataFrame using DataFrame.iloc[]
, DataFrame.drop()
and Dataframe.tail()
function with examples.
Happy Learning !!
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