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We'll look at using to get values ​​from cells in ilocPandas , which is great for selecting by position, and how it differs from . We'll also learn about the and methods, which we can use when we don't want to set the return type to .DataFramelociat['col_name'].values[]pandas.Series

ilocDataFrameGet value from cell in Pandas

iloc is the most efficient way to get values ​​from a cell in Pandas dataframe. Suppose, we have a DataFramedataframe with columns named priceand stockand want to get a value from the third row to check the price and stock availability.

First, we need to access the rows and then access the values ​​using the column names.

Sample code:

# python 3.x
import pandas as pd

df = pd.DataFrame(
    {
        "name": ["orange", "banana", "lemon", "mango", "apple"],
        "price": [2, 3, 7, 21, 11],
        "stock": ["Yes", "No", "Yes", "No", "Yes"],
    }
)
print(df.iloc[2]["price"])
print(df.iloc[2]["stock"])

Output:

7
Yes

ilocGet the row (or column) at a specific position in the index. That's why it only takes integers as parameters. Then locget the row (or column) with a specific label from the index.

Get values ​​from cells of a Pandas DataFrame using iatandat

iatand atare fast scalar access methods to DataFrameget values ​​from cells in Pandas.

Sample code:

# python 3.x
import pandas as pd

df = pd.DataFrame(
    {
        "name": ["orange", "banana", "lemon", "mango", "apple"],
        "price": [2, 3, 7, 21, 11],
        "stock": ["Yes", "No", "Yes", "No", "Yes"],
    }
)
print(df.iat[0, 0])
print(df.at[1, "stock"])

Output:

orange
No

To get the last row of entries, we will use at[df.index[-1],'stock'].

Sample code:

# python 3.x
import pandas as pd

df = pd.DataFrame(
    {
        "name": ["orange", "banana", "lemon", "mango", "apple"],
        "price": [2, 3, 7, 21, 11],
        "stock": ["Yes", "No", "Yes", "No", "Yes"],
    }
)
print(df.at[df.index[-1], "stock"])

Output:

Yes

df['col_name'].values[]Get Values ​​from Cells in a Pandas DataFrame

df['col_name'].values[]First datafarmeconvert the column to a one-dimensional array, then access the value at the index of that array:

Sample code:

# python 3.x
import pandas as pd

df = pd.DataFrame(
    {
        "name": ["orange", "banana", "lemon", "mango", "apple"],
        "price": [2, 3, 7, 21, 11],
        "stock": ["Yes", "No", "Yes", "No", "Yes"],
    }
)
print(df["stock"].values[0])

Output:

Yes

It returns nothing pandas.Series, and is the simplest to use.