2/12/22, 12:08 PM
Pandas Practice Code - Set 7 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 7.ipynb
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In [1]:
In [2]:
Out[1]:
Subject Score Gra de Remarks
0 Mathematics 85 B Good
1 History 98 A Excellent
2 English 76 C Fair
3 Science 72 C Fair
4 Arts 95 A Excellent
Out[2]:
Subject Science
Score 72
Grade C
Remarks Fair
Name: 3, dtype: object
# Indexing and Slicing Using iloc Function
# You can also use the iloc function for selecting and slicing
# records using index values. However, unlike the loc function,
# where you can pass both the string indexes and integer
# indexes, you can only pass the integer index values to the iloc
# function.
import pandas as pd
scores = [
{'Subject':'Mathematics', 'Score':85, 'Grade': 'B', 'Remarks': 'Good',
},
{'Subject':'History', 'Score':98, 'Grade': 'A','Remarks':
'Excellent'},
{'Subject':'English', 'Score':76, 'Grade': 'C','Remarks': 'Fair'},
{'Subject':'Science', 'Score':72, 'Grade': 'C','Remarks': 'Fair'},
{'Subject':'Arts', 'Score':95, 'Grade': 'A','Remarks': 'Excellent'},
]
my_df = pd.DataFrame(scores)
my_df.head()
# Let’s filter the record at index 3 (row 4).
my_df.iloc[3]
# The script below returns a series.
2/12/22, 12:08 PM
Pandas Practice Code - Set 7 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 7.ipynb
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In [3]:
In [4]:
In [5]:
Out[3]:
Subject Score Gra de Remarks
3 Science 72 C Fair
Out[4]:
Subject Score Gra de Remarks
2 English 76 C Fair
3 Science 72 C Fair
Out[5]:
Subject Score Gra de Remarks
2 English 76 C Fair
3 Science 72 C Fair
# If you want to select records from a single column as a
# dataframe, you need to specify the index inside the square
# brackets and then those square brackets inside the square
# brackets that follow the iloc function, as shown below.
my_df.iloc[[3]]
# You can pass multiple indexes to the iloc function to select
# multiple records. Here is an example:
my_df.iloc[[2,3]]
# You can also pass a range of indexes. In this case, the records
# from the lower range to 1 less than the upper range will be
# selected.
# For instance, the script below returns records from index 2 to
# index 3 (1 less than 4).
my_df.iloc[2:4]
2/12/22, 12:08 PM
Pandas Practice Code - Set 7 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 7.ipynb
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In [6]:
In [7]:
In [8]:
Out[6]:
Subject Score
2 English 76
3 Science 72
Out[7]:
Subject Score
2 English 76
3 Science 72
Out[8]:
Subject Score Gra de Remarks
0 Mathematics 85 B Good
2 English 76 C Fair
3 Science 72 C Fair
# In addition to specifying indexes, you can also pass column
# numbers (starting from 0) to the iloc method.
# The following script returns values from columns number 0
# and 1 for the records at indexes 2 and 3.
my_df.iloc[[2,3], [0,1]]
# You can also pass a range of indexes and columns to select.
# The script below selects columns 1 and 2 and rows 2 and 3.
my_df.iloc[2:4, 0:2]
# Dropping Rows and Columns with the drop() Method
# Apart from selecting columns using the loc and iloc functions,
# you can also use the drop() method to drop unwanted rows
# and columns from your dataframe while keeping the rest of
# the rows and columns.
# The following script drops records at indexes 1 and 4.
my_df2 = my_df.drop([1,4])
my_df2.head()
2/12/22, 12:08 PM
Pandas Practice Code - Set 7 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 7.ipynb
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In [9]:
In [10]:
Out[9]:
index Subject Score Grade Remarks
0 0 Mathematics 85 B Good
1 2 English 76 C Fair
2 3 Science 72 C Fair
Out[10]:
Subject Score Gra de Remarks
0 Mathematics 85 B Good
2 English 76 C Fair
3 Science 72 C Fair
# From the output above, you can see that the indexes are not
# in sequence since you have dropped indexes 1 and 4.
# You can reset dataframe indexes starting from 0, using the
# reset_index().
# Let’s call the reset_index() method on the my_df2 dataframe.
# Here, the value True for the inplace parameter specifies that
# you want to remove the records in place without assigning the
# result to any new variable.
my_df2.reset_index(inplace=True)
my_df2.head()
# The above output shows that the indexes have been reset.
# Also, you can see that a new column index has been added,
# which contains the original index. If you only want to reset
# new indexes without creating a new column named index, you
# can do so by passing True as the value for the drop parameter
# of the reset_index method.
# Let’s again drop some rows and reset the index using the
# reset_index() method by passing True as the value for the
# drop attribute. See the following two scripts:
my_df2 = my_df.drop([1,4])
my_df2.head()
2/12/22, 12:08 PM
Pandas Practice Code - Set 7 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 7.ipynb
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In [11]:
In [12]:
In [13]:
Out[11]:
Subject Score Gra de Remarks
0 Mathematics 85 B Good
1 English 76 C Fair
2 Science 72 C Fair
Out[12]:
Subject Score Gra de Remarks
0 Mathematics 85 B Good
1 History 98 A Excellent
2 English 76 C Fair
3 Science 72 C Fair
4 Arts 95 A Excellent
Out[13]:
Subject Score Gra de Remarks
0 Mathematics 85 B Good
2 English 76 C Fair
my_df2.reset_index(inplace=True, drop = True)
my_df2.head()
# By default, the drop method doesn’t drop rows in place.
# Instead, you have to assign the result of the drop() method to
# another variable that contains the records with dropped
# results.
# For instance, if you drop the records at indexes 1, 3, and 4
# using the following script and then print the dataframe
# header, you will see that the rows are not removed from the
# original dataframe.
my_df.drop([1,3,4])
my_df.head()
# If you want to drop rows in place, you need to pass True as
# the value for the inplace attribute, as shown in the script
# below:
my_df.drop([1,3,4], inplace = True)
my_df.head()
2/12/22, 12:08 PM
Pandas Practice Code - Set 7 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 7.ipynb
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