Info & Describe

The .info() method prints a concise summary of your DataFrame.

df.info()

Prints something like this:

<class 'pandas.DataFrame'>
RangeIndex: 20 entries, 0 to 19
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   city    20 non-null     object
 1   price   20 non-null     float64
 2   sqft    19 non-null     float64
dtypes: float64(2), object(1)
memory usage: 612.0 bytes

We get a bunch of useful info at once:

• The number of rows (in this example, 20)
• The number of columns (3)
• The column names and their data types (object, float64)
• The count of non-null values in each column (sqft has 19 non-null, meaning 1 missing value)
• The memory footprint of the DataFrame (612.0 bytes)

The .describe() method digs a bit deeper, generating descriptive statistics for numeric columns by default:

stats_df = df.describe()
print(stats_df)

This returns a new DataFrame containing the statistics. If you print it, the output will look something like this:

               price         sqft
count      20.000000    19.000000
mean   470250.000000  1575.263158
std    150939.818959   410.573696
min    225000.000000   880.000000
25%    373750.000000  1325.000000
50%    465000.000000  1550.000000
75%    548750.000000  1825.000000
max    815000.000000  2400.000000

• count: Number of non-null (non-missing) values
• mean: Average value
• std: Standard deviation (how spread out the values are; higher = more variation)
• min: Minimum (smallest) value
• 25%: First quartile (25% of values are below this)
• 50%: Median (middle value; half of values are above, half below)
• 75%: Third quartile (75% of values are below this)
• max: Maximum (largest) value

Because .describe() returns a DataFrame, you can select from it like any other DataFrame. The .loc[] indexer selects by label instead of numeric position.

stats = df.describe()
price_stats = stats["price"]
print(price_stats.loc["mean"])
# 470250.0

SnackStack's analytics team needs the average temperature for a new batch of sensor readings, after a quick structural check. Complete the get_average_temperature function. It accepts a DataFrame, prints its info, and returns the mean temperature.

1. Print the DataFrame's info with .info().
2. Get the descriptive statistics with .describe().
3. Select the "temperature" column from the stats, grab its "mean" with .loc[], and return that rounded to two decimal places.