![AGGREGATION
• Aggregation functions allow you to summarize the data in a DataFrame by calculating a single
value for each column.
• The most common aggregation functions are:
sum(), mean(), median(), mode(), std(), and var().
To use an aggregation function, you can pass it to the agg() method of a DataFrame.
For example, to calculate the mean of each column in a DataFrame, you would use the following
code:
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df.agg(['mean'])](https://image.slidesharecdn.com/datascienceppt-240527140933-6434f7f4/85/Data-Science-ppt-on-dataframe-operations-pptx-2-320.jpg)
![GROUP BY
• The groupby() method allows you to group the rows in a DataFrame by one or
more columns.
• This can be useful for performing aggregation operations on specific groups of
data.
• For example, to calculate the mean of each column in a DataFrame, grouped by
the "A" column, you would use the following code:
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df.groupby('A').agg(['mean'])](https://image.slidesharecdn.com/datascienceppt-240527140933-6434f7f4/85/Data-Science-ppt-on-dataframe-operations-pptx-3-320.jpg)
![SORTING
• The sort_values() method allows you to sort the rows in a DataFrame by one or more
columns.
• You can specify whether to sort in ascending or descending order by passing the
ascending parameter to the sort_values() method.
• For example, to sort the rows in a DataFrame by the "A" column in ascending order,
you would use the following code
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df.sort_values('A', ascending=True)](https://image.slidesharecdn.com/datascienceppt-240527140933-6434f7f4/85/Data-Science-ppt-on-dataframe-operations-pptx-4-320.jpg)
![RENAMING INDEX
• The set_index() method allows you to rename the index of a DataFrame.
• You can pass a column name or a list of column names to the set_index() method.
• For example, to rename the index of a DataFrame to the "A" column, you would
use the following code:
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df.set_index('A')](https://image.slidesharecdn.com/datascienceppt-240527140933-6434f7f4/85/Data-Science-ppt-on-dataframe-operations-pptx-5-320.jpg)
![PIVOTING
• The pivot_table() method allows you to reshape a DataFrame by pivoting the rows
and columns.
• This can be useful for creating summary tables and cross-tabulations.
• For example, to create a summary table of the mean of each column in a
DataFrame, grouped by the "A" column, you would use the following code
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df.pivot_table(values='B', index='A', aggfunc='mean')](https://image.slidesharecdn.com/datascienceppt-240527140933-6434f7f4/85/Data-Science-ppt-on-dataframe-operations-pptx-6-320.jpg)
Data Science ppt on dataframe operations.pptx
- 2. AGGREGATION • Aggregation functions allow you to summarize the data in a DataFrame by calculating a single value for each column. • The most common aggregation functions are: sum(), mean(), median(), mode(), std(), and var(). To use an aggregation function, you can pass it to the agg() method of a DataFrame. For example, to calculate the mean of each column in a DataFrame, you would use the following code: import pandas as pd df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df.agg(['mean'])
- 3. GROUP BY • The groupby() method allows you to group the rows in a DataFrame by one or more columns. • This can be useful for performing aggregation operations on specific groups of data. • For example, to calculate the mean of each column in a DataFrame, grouped by the "A" column, you would use the following code: import pandas as pd df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df.groupby('A').agg(['mean'])
- 4. SORTING • The sort_values() method allows you to sort the rows in a DataFrame by one or more columns. • You can specify whether to sort in ascending or descending order by passing the ascending parameter to the sort_values() method. • For example, to sort the rows in a DataFrame by the "A" column in ascending order, you would use the following code import pandas as pd df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df.sort_values('A', ascending=True)
- 5. RENAMING INDEX • The set_index() method allows you to rename the index of a DataFrame. • You can pass a column name or a list of column names to the set_index() method. • For example, to rename the index of a DataFrame to the "A" column, you would use the following code: import pandas as pd df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df.set_index('A')
- 6. PIVOTING • The pivot_table() method allows you to reshape a DataFrame by pivoting the rows and columns. • This can be useful for creating summary tables and cross-tabulations. • For example, to create a summary table of the mean of each column in a DataFrame, grouped by the "A" column, you would use the following code import pandas as pd df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df.pivot_table(values='B', index='A', aggfunc='mean')