How to add a new column to an existing DataFrame?

Deeper dive: different techniques to add columns

Add new columns using the assign method

The assign method is significantly useful when you intend to return a new DataFrame:

df = df.assign(new_column_name=values)
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Use assign to avoid uninvited guests like SettingWithCopyWarning. It's also perfect for method cocktails 🍹 - you can chain it with other methods!

Precise Additions using .loc

To insert values at specific locations, .loc works like a charm:

df.loc[:, 'new_column'] = value  # Assigns one value to all rows
df.loc[:, 'new_column'] = np.random.randn(len(df))  # Magic spell for random values
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Ensure the spell sequence is as long as the DataFrame to cast without mismatch errors.

Mind the Indexes

When adding a Series, make sure the Series index and DataFrame index are on the same page. If you're unsure, you can always reset_index:

df = df.reset_index()
df['new_column'] = pd.Series(values)
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Quick tips for smoothly adding columns

Jerry-rig keyword collision

Exercise caution with column names to avoid any clashes with Python keywords or built-in functions. Name collision can lead to a lot of "why is this not working?!" moments.

Keep your data formatting consistent

Keep an eye on the format and structure of your existing data. Newly-added columns should match the live band, not play their own genre. 💃

Method chaining for DataFrame integrity

df.assign is your friend when it comes to method chaining. It allows you to add multiple columns without disturbing your DataFrame's beauty sleep.

Index alignment and performance

Friendly conversion to native types

In case of potential index mismatch, feel free to convert Series to a numpy array or list:

df['new_column'] = pd.Series(values).to_numpy()
# or mimic Sachin Tendulkar's straight-drive
df['new_column'] = pd.Series(values).tolist()
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Explicit index matchmaking

It's a date! Set the Series index and DataFrame's index on a romantic candlelit dinner:

new_series = pd.Series(values, index=df.index)
df['new_column'] = new_series
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Multitasking with multiple new columns

assign is a superstar when adding multiple columns simultaneously:

df = df.assign(new_col1=values1, new_col2=values2)
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Efficiency always wins

Keep up-to-date with the pandas documentation for faster and efficient ways of adding cookie dough - ehh, I mean, columns! Especially for large DataFrames.