To add values into columns in Pandas, you can simply assign a list of values to the desired column using bracket notation. For example, you can create a new column named 'new_column' and assign a list of values to it like this: df['new_column'] = [1, 2, 3, 4, 5]. This will add the values 1, 2, 3, 4, and 5 into the 'new_column' of the Pandas DataFrame df. You can also add values to existing columns by assigning new values to them using bracket notation.
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How to add values into columns with datetime index in pandas?
To add values into columns with a datetime index in pandas, you can use the following steps:
- Create a DataFrame with a datetime index:
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import pandas as pd dates = pd.date_range('2022-01-01', periods=5) df = pd.DataFrame(index=dates) |
- Add values into columns by specifying the column name and assigning the values:
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df['column_name'] = [1, 2, 3, 4, 5]
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- If you want to add values to a specific row, you can specify the row index along with the column name:
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df.at['2022-01-02', 'column_name'] = 10
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- You can also add values to multiple columns at once by passing a dictionary of values:
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data = {'column1': [10, 20, 30, 40, 50], 'column2': [100, 200, 300, 400, 500]} df = pd.DataFrame(data, index=dates) |
- Finally, you can update the values in a column by reassigning new values to that column:
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df['column_name'] = [10, 20, 30, 40, 50]
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By following these steps, you can easily add values into columns with a datetime index in pandas.
How to add constant values into columns in pandas?
You can add constant values into columns in pandas by using the assign method along with the pd.Series constructor.
Here is an example:
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import pandas as pd # create a DataFrame df = pd.DataFrame({ 'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8] }) # add a constant value to a new column df = df.assign(C = 10) # add a constant value to an existing column df['D'] = 20 print(df) |
This will output:
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A B C D 0 1 5 10 20 1 2 6 10 20 2 3 7 10 20 3 4 8 10 20 |
In this example, the constant value 10 is added to a new column C using assign method and to an existing column D by directly assigning the value.
How to add values into columns based on conditions in pandas?
To add values into columns based on conditions in pandas, you can use the numpy.where function.
Here's an example:
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import pandas as pd import numpy as np # Create a sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]} df = pd.DataFrame(data) # Add a new column 'C' with values based on a condition df['C'] = np.where(df['A'] > 3, 'Yes', 'No') print(df) |
This code will create a new column 'C' in the DataFrame df where the value is 'Yes' if the value in column 'A' is greater than 3, and 'No' otherwise.
You can modify the condition and the value to be added based on your specific requirements.
How to add values into columns in pandas using at?
You can add values into specific columns in a pandas DataFrame using the at method.
Here's an example:
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import pandas as pd # Creating a sample DataFrame data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]} df = pd.DataFrame(data) # Adding a new value to column 'A' at index 2 df.at[2, 'A'] = 99 print(df) |
Output:
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A B 0 1 5 1 2 6 2 99 7 3 4 8 |
In this example, we used the at method to add the value 99 to column 'A' at index 2 in the DataFrame.
