How to add an empty column to a dataframe?

Don't resist, persist! More ways to make a column exist

NaN is your numerical panacea

Numeric data types, fear not! Adding an empty column with np.nan is like adding a value of infinity - hard to comprehend but easy to compute with. It's perfect for data analysis and won't cause errors when performing mathematical operations.

import numpy as np
df['numeric_column'] = np.nan     # Infinity and beyond... well, not really, but close.
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Chain reaction with assign

Prefer to keep your code clean and tidy? Use the assign method to stage a friendly protest against cluttered code. It supports chaining and provides you with an all new DataFrame, fresh from the oven.

df = df.assign(new_text_column='').assign(new_numeric_column=np.nan)  # Two for the price of one!
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Explicit datatype - A column's identity crisis solver

To explicitly define your column type, use the pd.Series with a dtype. This is the scholarly professor approach: always precise, concise, and despises surprises.

df['typed_column'] = pd.Series(dtype='float64')       # Always give due credit to dtype.
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When one parking space is not enough...

Need to add more empty columns to your DataFrame? It's like preparing a bigger parking lot; simply expand your DataFrame with new parking spaces!

new_cols = ['col1', 'col2', 'col3']
df = df.reindex(columns=df.columns.tolist() + new_cols)   # Your parking space is now ready for the party!
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Enter DataFrame Concatenation

Sometimes, it's just easier to construct an empty DataFrame and append it to the original DataFrame. DataFrame concatenation is the equivalent of adding a whole new parking lot:

new_columns = pd.DataFrame(columns=['new_col1', 'new_col2'])
df = pd.concat([df, new_columns], axis=1)  # DataFrame merging: Your one-stop solution to new columns.
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The dilemma of data types

Always choose your data type wisely. Choose pd.NA for a type-agnostic approach (like a one-size-fits-all T-shirt), but be careful when your column is strictly for numerical data. np.nan is the safer option when you want to keep everything consistent with recent numeric data.