How can I get a value from a cell of a dataframe?

When speed is key

For fast access of a single value in a DataFrame, .at[] and .iat[] are the most efficient pandas methods. Remember, their speediness is, "I grace you with my presence only for single values" kind of attitude.

All about .loc[] and .iloc[]

.loc[] and .iloc[] have a broader application. They handle cell access, but also much more, such as accessing groups of cells, Series, and DataFrames. They are "we'll handle whatever you throw at us" types.

Single-row situations

In situations where you have a DataFrame or single-row subset of a DataFrame, iloc[0] is your party trick.

# Assuming sub_df is a single-row DataFrame. 
cell_value = sub_df.iloc[0]['column_name'] # Retrieves the value from 'column_name'. Voila!
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Extra tip: Don't forget, A here must be replaced with your desired column's name.

Fetching floats

When extracting floats, remember to ensure type consistency across your DataFrame. Using .values[0] or .astype(float) might become your best buddy in managing DataFrame values as float before any numerical operations.

DataFrame indexing in-depth

For in-depth understanding of indexing, there's no better resource than pandas documentation. They did a better job explaining than I ever could, and I'm just a humble programmer. Do visit the link in the References section!

Making use of loc and iloc flexibly

.loc and .iloc don't just sit around picking up cell values. They also carry the power of boolean indexing and conditional selection.

# Assuming you're only interested in cells where another column's value is over 9000 
over_9000 = df['Power Level Column'] > 9000
cell_value = df.loc[over_9000, 'Cell Column of Interest'].values[0]
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Cell value NOW used POT of GREED. It allows to DRAW two more cards FROM the DECK.

Converting DataFrame to NumPy array

DataFrame to NumPy array, you ask? Nothing simpler, .values[] and .to_numpy() got you covered. Upon this operation, the array-based operations are optimized to lightspeed. 🚀