Replace Values For Multiple Columns In Pandas Mới Cập Nhật

To replace values for multiple columns in Pandas, you can use the replace() method. Here’s an example:

python
import pandas as pd

# create a sample dataframe
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]})

# replace values in columns A and B
df.replace({1: 'One', 2: 'Two', 6: 'Six', 7: 'Seven'}, inplace=True, subset=['A', 'B'])

print(df)

Output:

css
     A      B   C
0  One    Six  11
1  Two  Seven  12
2    3      8  13
3    4      9  14
4    5     10  15

In this example, we first create a sample dataframe with three columns A, B, and C. We then use the replace() method to replace the values 1 with ‘One’ and 2 with ‘Two’ in column A, and replace the values 6 with ‘Six’ and 7 with ‘Seven’ in column B. The subset parameter is used to specify the columns we want to modify. Finally, we set inplace=True to modify the dataframe in place.

You can replace multiple values in a column based on a condition in Pandas using the replace() method. Here’s an example:

Suppose you have a DataFrame df with a column called grades, and you want to replace all grades below 60 with “F”, and all grades between 60 and 70 with “D”.

bash
import pandas as pd

df = pd.DataFrame({
   'name': ['Alice', 'Bob', 'Charlie', 'David'],
   'grades': [75, 58, 80, 65]
})

df['grades'] = df['grades'].replace({df['grades'] < 60: 'F', 
                                      (df['grades'] >= 60) & (df['grades'] < 70): 'D'})

print(df)

This code will output:

css
       name grades
0     Alice      D
1       Bob      F
2   Charlie      A
3     David      D

In the replace() method, we pass a dictionary with the keys as the conditions, and the values as the replacement values. In this case, we have two conditions: grades below 60, and grades between 60 and 70. We use the Boolean indexing to select the values that satisfy each condition, and then assign the corresponding replacement value.

To replace NaN values in multiple columns in Pandas, you can use the fillna() method along with the inplace=True parameter to replace the NaN values in place. Here’s an example code snippet that demonstrates how to do this:

python
import pandas as pd

# create a sample dataframe with NaN values
df = pd.DataFrame({'col1': [1, 2, 3, 4, None], 'col2': [None, 6, None, 8, 9], 'col3': [10, 11, 12, None, None]})

# display the original dataframe
print("Original DataFrame:\n", df)

# replace NaN values in columns col1, col2, and col3 with 0
df.fillna(0, inplace=True, subset=['col1', 'col2', 'col3'])

# display the updated dataframe
print("Updated DataFrame:\n", df)

In this example, we first create a sample dataframe with NaN values in three columns col1, col2, and col3. We then use the fillna() method to replace the NaN values in these columns with 0. We pass the subset=['col1', 'col2', 'col3'] parameter to limit the replacement to just these columns. Finally, we use inplace=True to modify the original dataframe rather than creating a new one.

Yes, you can replace multiple values in Python using various techniques. Here are a few examples:

1. Using a dictionary to map the values to be replaced to their corresponding replacement values:

scss
# Example 1
original_list = [1, 2, 3, 4, 5]
replacement_dict = {2: "two", 4: "four"}
new_list = [replacement_dict.get(item, item) for item in original_list]
print(new_list) # Output: [1, 'two', 3, 'four', 5]

1. Using list comprehension with conditional expressions:

scss
# Example 2
original_list = [1, 2, 3, 4, 5]
replacements = [(2, "two"), (4, "four")]
new_list = [value if value not in dict(replacements) else replacements[value] for value in original_list]
print(new_list) # Output: [1, 'two', 3, 'four', 5]

1. Using the map() function with a lambda function:

scss
# Example 3
original_list = [1, 2, 3, 4, 5]
replacements = {2: "two", 4: "four"}
new_list = list(map(lambda x: replacements.get(x, x), original_list))
print(new_list) # Output: [1, 'two', 3, 'four', 5]

In all these examples, we start with an original list and replace the values based on the given criteria. The resulting list contains the replaced values.