How to Format A Dataframe Column Wise In Pandas?

To format a dataframe column-wise in pandas, you can use the df.applymap() function along with a lambda function to apply the formatting you desire to each element in the dataframe. This allows you to apply different formatting to different columns.

For example, to format a column as a percentage (%), you can use:

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df['column_name'] = df['column_name'].applymap(lambda x: '{:.2f}%'.format(x))

To format a column as a currency, you can use:

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df['column_name'] = df['column_name'].applymap(lambda x: '${:,.2f}'.format(x))

You can also apply other formatting styles as needed using this approach. Just replace the lambda function with the desired formatting style.

Remember to replace 'column_name' with the actual name of the column you want to format in your dataframe.

What is the difference between merge and join in Pandas?

In Pandas, both merge and join are used to combine data from different DataFrame objects, but they have some differences:

1. Merge: Merge is a method in Pandas that is used to combine two DataFrame objects based on a common column or index. It is more flexible and allows for different types of joins such as inner, outer, left, and right joins. You can specify the column(s) to join on using the "on" parameter.
2. Join: Join is a method in Pandas that is used to combine two DataFrame objects based on their index. It is more limited in terms of the types of joins it can perform, as it only supports left, right, inner, and outer joins. The default type of join in the join method is a left join.

In summary, the main difference between merge and join in Pandas is that merge is more versatile and allows for joining on both columns and indexes, while join is more limited and only allows for joining on indexes.

How to extract specific rows from a Pandas DataFrame?

To extract specific rows from a Pandas DataFrame, you can use boolean indexing or the iloc method.

1. Using boolean indexing: You can use boolean indexing to filter rows based on a specific condition. For example, if you want to extract rows where the value in the 'column_name' column is greater than 50, you can do the following:

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import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'column_name': [10, 20, 30, 40, 50, 60, 70, 80, 90]})

# Filter rows where the value in the 'column_name' column is greater than 50
specific_rows = df[df['column_name'] > 50]
print(specific_rows)

1. Using iloc method: You can also use the iloc method to extract specific rows by their index position. For example, if you want to extract rows 2 to 5 from the DataFrame, you can do the following:

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import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'column_name': [10, 20, 30, 40, 50, 60, 70, 80, 90]})

# Extract rows 2 to 5
specific_rows = df.iloc[2:6]
print(specific_rows)

These are two ways to extract specific rows from a Pandas DataFrame. You can choose the method that best suits your requirements.

How to get the unique values of a column in a Pandas DataFrame?

You can get the unique values of a column in a Pandas DataFrame by using the unique() method. Here is an example:

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import pandas as pd

# Create a sample DataFrame
data = {'col1': [1, 2, 3, 2, 1, 3, 4, 5]}
df = pd.DataFrame(data)

# Get the unique values of 'col1'
unique_values = df['col1'].unique()

print(unique_values)

This will output:

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[1 2 3 4 5]

The unique() method returns an array of unique values in the specified column of the DataFrame.

How to convert a Pandas DataFrame to a NumPy array?

You can convert a Pandas DataFrame to a NumPy array by using the values attribute of the DataFrame. Here is an example:

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import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3, 4],
        'B': [5, 6, 7, 8]}
df = pd.DataFrame(data)

# Convert the DataFrame to a NumPy array
array = df.values

print(array)

This will output:

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[[1 5]
 [2 6]
 [3 7]
 [4 8]]