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DataFramesTopic 13 of 836 min read

Inspecting Data

The first five commands on any new dataset: head, tail, sample, info and describe (including include='all'), plus memory_usage.

Whenever you load a new dataset — an IPL ball-by-ball file, a car-price CSV, anything — you should inspect it before doing any analysis. Data scientists run the same handful of commands every single time: look at a few rows, check the columns and types, count missing values, and see the basic statistics. Skipping this step is how wrong conclusions happen.

Definition

Inspecting a DataFrame means examining its structure and contents without changing it. head/tail/sample show rows; info shows the structure (columns, non-null counts, dtypes, memory); describe shows summary statistics; memory_usage shows bytes per column.

Without changing it — none of these methods modify df.
Structure vs statistics — info answers "what is in this table?", describe answers "what do the numbers look like?".

Syntax

python

df.head(n=5)        df.tail(n=5)        df.sample(n=None, frac=None, random_state=None)
df.info(verbose=None, memory_usage=None, show_counts=None)
df.describe(percentiles=None, include=None, exclude=None)
df.memory_usage(index=True, deep=False)

Method / parameter	Meaning	Default
`head(n)` / `tail(n)`	first / last `n` rows (`head(-2)` = all rows except the last 2)	`5`
`sample(n)` / `sample(frac=...)`	random rows / random fraction of rows	`1` row
`random_state`	seed for repeatable sampling	`None`
`info(memory_usage="deep")`	measure the real memory of text columns	estimate
`describe(include=...)`	`"all"`, `"number"`, `"str"`, `"category"`, or a list of dtypes	numeric columns only
`describe(exclude=...)`	dtypes to leave out	`None`
`describe(percentiles=[...])`	which percentiles to show	`[.25, .5, .75]`
`memory_usage(deep=True)`	include the real size of text values	`False`

Example — head, tail, sample

python

print(df.head(3))
print(df.tail(2))
print(df.sample(3, random_state=7))

text

           player team  matches  runs  strike_rate
0    Shubman Gill   GT       17   890        157.8
1  Faf du Plessis  RCB       14   730        153.7
2    Devon Conway  CSK       16   672        139.7
            player team  matches  runs  strike_rate
6  Ruturaj Gaikwad  CSK       16   590        147.5
7      Rinku Singh  KKR       14   474        149.5
             player team  matches  runs  strike_rate
2      Devon Conway  CSK       16   672        139.7
5  Suryakumar Yadav   MI       16   605        181.1
0      Shubman Gill   GT       17   890        157.8

How it works: head shows the top of the file (are the column names right? did the header load?), tail the bottom (is there a "Total" row or junk at the end?), and sample random rows from the middle — files are often sorted, so the first rows can be misleading.

Example — info()

python

df.info()

text

<class 'pandas.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype
---  ------       --------------  -----
 0   player       8 non-null      str
 1   team         8 non-null      str
 2   matches      8 non-null      int64
 3   runs         8 non-null      int64
 4   strike_rate  8 non-null      float64
dtypes: float64(1), int64(2), str(2)
memory usage: 452.0 bytes

Line	Tells you
`RangeIndex: 8 entries, 0 to 7`	number of rows and the index type
`Data columns (total 5 columns)`	number of columns
`Non-Null Count`	how many values are present in each column → compare with the row count to spot missing data
`Dtype`	the type of each column — numbers stored as `str` show up here
`dtypes: ...`	how many columns of each type
`memory usage`	approximate memory used

info() prints its report and returns None. Here is the same check on a table with missing values:

text

           player team  matches  runs  strike_rate
0    Shubman Gill   GT       17   890        157.8
1  Faf du Plessis  RCB       14   730          NaN
2    Devon Conway  CSK       16   672        139.7
3     Virat Kohli  NaN       14   639        139.8

text

<class 'pandas.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype
---  ------       --------------  -----
 0   player       4 non-null      str
 1   team         3 non-null      str
 2   matches      4 non-null      int64
 3   runs         4 non-null      int64
 4   strike_rate  3 non-null      float64
dtypes: float64(1), int64(2), str(2)
memory usage: 292.0 bytes

team and strike_rate show 3 non-null out of 4 entries → each has one missing value.

Example — describe()

python

print(df.describe())

text

         matches        runs  strike_rate
count   8.000000    8.000000     8.000000
mean   15.125000  653.125000   154.087500
std     1.246423  120.565853    13.678287
min    14.000000  474.000000   139.700000
25%    14.000000  601.250000   145.575000
50%    15.000000  632.000000   151.600000
75%    16.000000  686.500000   159.250000
max    17.000000  890.000000   181.100000

How it works: by default describe summarises only numeric columns (player and team are skipped). For each: count (non-missing), mean, std (spread), min, the 25th/50th(median)/75th percentiles, and max. Reading it: the median run tally (632) is below the mean (653) because one big value (Gill's 890) pulls the mean up. Add .round(1) for a cleaner view, or .T to flip it when there are many columns.

Example — describe(include="all")

python

print(df.describe(include="all"))

text

              player team    matches        runs  strike_rate
count              8    8   8.000000    8.000000     8.000000
unique             8    6        NaN         NaN          NaN
top     Shubman Gill  RCB        NaN         NaN          NaN
freq               1    2        NaN         NaN          NaN
mean             NaN  NaN  15.125000  653.125000   154.087500
std              NaN  NaN   1.246423  120.565853    13.678287
min              NaN  NaN  14.000000  474.000000   139.700000
25%              NaN  NaN  14.000000  601.250000   145.575000
50%              NaN  NaN  15.000000  632.000000   151.600000
75%              NaN  NaN  16.000000  686.500000   159.250000
max              NaN  NaN  17.000000  890.000000   181.100000

How it works: include="all" adds the text columns. Text columns get count, unique (distinct values), top (most frequent) and freq (its count); numeric columns get the usual stats. Statistics that do not apply show NaN. To see only the text columns use df.describe(include="str") (or exclude="number").

Note (pandas 3): old tutorials write describe(include="object") for text columns. In pandas 3 text is str, so use include="str" — the "object" form still works for now but raises a deprecation warning. Also, custom percentiles=[0.1, 0.9] now shows exactly those percentiles (the 50% row is no longer added automatically).

Example — memory_usage

python

print(df.memory_usage(deep=True))

text

Index          132
player         499
team           413
matches         64
runs            64
strike_rate     64
dtype: int64

How it works: each numeric column uses 8 rows × 8 bytes = 64 bytes. Text columns are larger and their true size is only measured with deep=True (without it, pandas shows a rough estimate). df.memory_usage(deep=True).sum() gives the total in bytes — useful for spotting columns to convert to category on big datasets.

The inspection checklist

text

1. df.shape                 how big is it?
2. df.head() / df.sample()  does it look right?
3. df.info()                column types + missing values
4. df.describe()            ranges, outliers, suspicious min/max
5. df.isna().sum()          missing values per column (data-cleaning chapter)
6. df["col"].value_counts() categories in a text column

Common mistakes

❌ print(df.info()) → prints the report and then None. ✅ Just call df.info().
❌ Assuming describe() covers all columns. ✅ It is numeric-only by default; use include="all".
❌ Judging a sorted dataset by head() alone. ✅ Also check tail() and sample().
❌ Ignoring a numeric-looking column with dtype str in info(). ✅ Convert it (see "Data Types & astype").

Interview tip

Q: You receive a new CSV. What are the first things you run? A: df.shape, df.head() and df.sample() to see the data, df.info() for column types and non-null counts, df.describe(include="all") for ranges and categories, and df.isna().sum() to quantify missing values — before any cleaning or analysis.

Key points to remember

head(n)/tail(n) show the first/last rows; sample(n, random_state=…) shows random rows.
info() prints row count, column names, non-null counts, dtypes and memory, and returns None.
Compare each column's non-null count with the row count to spot missing values.
describe() summarises numeric columns: count, mean, std, min, 25%, 50%, 75%, max.
describe(include='all') adds text columns with count, unique, top and freq.
In pandas 3 use include='str' (not 'object') to describe text columns.
memory_usage(deep=True) shows the real bytes used by each column.

Try it yourself

First look: head, tail, sample, info

import pandas as pd

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli",
               "Yashasvi Jaiswal", "Suryakumar Yadav", "Ruturaj Gaikwad", "Rinku Singh"],
    "team": ["GT", "RCB", "CSK", "RCB", "RR", "MI", "CSK", "KKR"],
    "matches": [17, 14, 16, 14, 14, 16, 16, 14],
    "runs": [890, 730, 672, 639, 625, 605, 590, 474],
    "strike_rate": [157.8, 153.7, 139.7, 139.8, 163.6, 181.1, 147.5, 149.5],
})
print(df.head(3))
print(df.tail(2))
print(df.sample(2, random_state=7))
df.info()

import pandas as pd

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli",
               "Yashasvi Jaiswal", "Suryakumar Yadav", "Ruturaj Gaikwad", "Rinku Singh"],
    "team": ["GT", "RCB", "CSK", "RCB", "RR", "MI", "CSK", "KKR"],
    "matches": [17, 14, 16, 14, 14, 16, 16, 14],
    "runs": [890, 730, 672, 639, 625, 605, 590, 474],
    "strike_rate": [157.8, 153.7, 139.7, 139.8, 163.6, 181.1, 147.5, 149.5],
})
print(df.head(3))
print(df.tail(2))
print(df.sample(2, random_state=7))
df.info()

describe: numbers only vs include='all'

import pandas as pd

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli",
               "Yashasvi Jaiswal", "Suryakumar Yadav", "Ruturaj Gaikwad", "Rinku Singh"],
    "team": ["GT", "RCB", "CSK", "RCB", "RR", "MI", "CSK", "KKR"],
    "matches": [17, 14, 16, 14, 14, 16, 16, 14],
    "runs": [890, 730, 672, 639, 625, 605, 590, 474],
    "strike_rate": [157.8, 153.7, 139.7, 139.8, 163.6, 181.1, 147.5, 149.5],
})
print(df.describe().round(1))
print()
print(df.describe(include="str"))
print()
print(df.describe(include="all").round(1))

import pandas as pd

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli",
               "Yashasvi Jaiswal", "Suryakumar Yadav", "Ruturaj Gaikwad", "Rinku Singh"],
    "team": ["GT", "RCB", "CSK", "RCB", "RR", "MI", "CSK", "KKR"],
    "matches": [17, 14, 16, 14, 14, 16, 16, 14],
    "runs": [890, 730, 672, 639, 625, 605, 590, 474],
    "strike_rate": [157.8, 153.7, 139.7, 139.8, 163.6, 181.1, 147.5, 149.5],
})
print(df.describe().round(1))
print()
print(df.describe(include="str"))
print()
print(df.describe(include="all").round(1))

Spot missing values and memory

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli"],
    "team": ["GT", "RCB", "CSK", None],
    "runs": [890, 730, 672, 639],
    "strike_rate": [157.8, np.nan, 139.7, 139.8],
})
df.info()
print()
print(df.memory_usage(deep=True))
print("Total bytes:", df.memory_usage(deep=True).sum())

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli"],
    "team": ["GT", "RCB", "CSK", None],
    "runs": [890, 730, 672, 639],
    "strike_rate": [157.8, np.nan, 139.7, 139.8],
})
df.info()
print()
print(df.memory_usage(deep=True))
print("Total bytes:", df.memory_usage(deep=True).sum())

Practice: inspect a bowling table

# 🏏 Your turn: add a bowler with a missing economy (None), then Run and read info() and describe().
import pandas as pd

bowl = pd.DataFrame({
    "bowler": ["Mohammed Shami", "Mohit Sharma", "Rashid Khan", "Piyush Chawla"],
    "team": ["GT", "GT", "GT", "MI"],
    "wickets": [28, 27, 27, 22],
    "economy": [8.03, 8.17, 8.24, 8.11],
})
bowl.info()
print(bowl.describe(include="all"))

# 🏏 Your turn: add a bowler with a missing economy (None), then Run and read info() and describe().
import pandas as pd

bowl = pd.DataFrame({
    "bowler": ["Mohammed Shami", "Mohit Sharma", "Rashid Khan", "Piyush Chawla"],
    "team": ["GT", "GT", "GT", "MI"],
    "wickets": [28, 27, 27, 22],
    "economy": [8.03, 8.17, 8.24, 8.11],
})
bowl.info()
print(bowl.describe(include="all"))

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Opening notes

Notes PandasDataFrames

DataFramesTopic 13 of 836 min read

Inspecting Data

The first five commands on any new dataset: head, tail, sample, info and describe (including include='all'), plus memory_usage.

Definition

Inspecting a DataFrame means examining its structure and contents without changing it. head/tail/sample show rows; info shows the structure (columns, non-null counts, dtypes, memory); describe shows summary statistics; memory_usage shows bytes per column.

Without changing it — none of these methods modify df.
Structure vs statistics — info answers "what is in this table?", describe answers "what do the numbers look like?".

Syntax

python

df.head(n=5)        df.tail(n=5)        df.sample(n=None, frac=None, random_state=None)
df.info(verbose=None, memory_usage=None, show_counts=None)
df.describe(percentiles=None, include=None, exclude=None)
df.memory_usage(index=True, deep=False)

Method / parameter	Meaning	Default
`head(n)` / `tail(n)`	first / last `n` rows (`head(-2)` = all rows except the last 2)	`5`
`sample(n)` / `sample(frac=...)`	random rows / random fraction of rows	`1` row
`random_state`	seed for repeatable sampling	`None`
`info(memory_usage="deep")`	measure the real memory of text columns	estimate
`describe(include=...)`	`"all"`, `"number"`, `"str"`, `"category"`, or a list of dtypes	numeric columns only
`describe(exclude=...)`	dtypes to leave out	`None`
`describe(percentiles=[...])`	which percentiles to show	`[.25, .5, .75]`
`memory_usage(deep=True)`	include the real size of text values	`False`

Example — head, tail, sample

python

print(df.head(3))
print(df.tail(2))
print(df.sample(3, random_state=7))

text

           player team  matches  runs  strike_rate
0    Shubman Gill   GT       17   890        157.8
1  Faf du Plessis  RCB       14   730        153.7
2    Devon Conway  CSK       16   672        139.7
            player team  matches  runs  strike_rate
6  Ruturaj Gaikwad  CSK       16   590        147.5
7      Rinku Singh  KKR       14   474        149.5
             player team  matches  runs  strike_rate
2      Devon Conway  CSK       16   672        139.7
5  Suryakumar Yadav   MI       16   605        181.1
0      Shubman Gill   GT       17   890        157.8

Example — info()

python

df.info()

text

<class 'pandas.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype
---  ------       --------------  -----
 0   player       8 non-null      str
 1   team         8 non-null      str
 2   matches      8 non-null      int64
 3   runs         8 non-null      int64
 4   strike_rate  8 non-null      float64
dtypes: float64(1), int64(2), str(2)
memory usage: 452.0 bytes

Line	Tells you
`RangeIndex: 8 entries, 0 to 7`	number of rows and the index type
`Data columns (total 5 columns)`	number of columns
`Non-Null Count`	how many values are present in each column → compare with the row count to spot missing data
`Dtype`	the type of each column — numbers stored as `str` show up here
`dtypes: ...`	how many columns of each type
`memory usage`	approximate memory used

info() prints its report and returns None. Here is the same check on a table with missing values:

text

           player team  matches  runs  strike_rate
0    Shubman Gill   GT       17   890        157.8
1  Faf du Plessis  RCB       14   730          NaN
2    Devon Conway  CSK       16   672        139.7
3     Virat Kohli  NaN       14   639        139.8

text

<class 'pandas.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype
---  ------       --------------  -----
 0   player       4 non-null      str
 1   team         3 non-null      str
 2   matches      4 non-null      int64
 3   runs         4 non-null      int64
 4   strike_rate  3 non-null      float64
dtypes: float64(1), int64(2), str(2)
memory usage: 292.0 bytes

team and strike_rate show 3 non-null out of 4 entries → each has one missing value.

Example — describe()

python

print(df.describe())

text

         matches        runs  strike_rate
count   8.000000    8.000000     8.000000
mean   15.125000  653.125000   154.087500
std     1.246423  120.565853    13.678287
min    14.000000  474.000000   139.700000
25%    14.000000  601.250000   145.575000
50%    15.000000  632.000000   151.600000
75%    16.000000  686.500000   159.250000
max    17.000000  890.000000   181.100000

Example — describe(include="all")

python

print(df.describe(include="all"))

text

              player team    matches        runs  strike_rate
count              8    8   8.000000    8.000000     8.000000
unique             8    6        NaN         NaN          NaN
top     Shubman Gill  RCB        NaN         NaN          NaN
freq               1    2        NaN         NaN          NaN
mean             NaN  NaN  15.125000  653.125000   154.087500
std              NaN  NaN   1.246423  120.565853    13.678287
min              NaN  NaN  14.000000  474.000000   139.700000
25%              NaN  NaN  14.000000  601.250000   145.575000
50%              NaN  NaN  15.000000  632.000000   151.600000
75%              NaN  NaN  16.000000  686.500000   159.250000
max              NaN  NaN  17.000000  890.000000   181.100000

Note (pandas 3): old tutorials write describe(include="object") for text columns. In pandas 3 text is str, so use include="str" — the "object" form still works for now but raises a deprecation warning. Also, custom percentiles=[0.1, 0.9] now shows exactly those percentiles (the 50% row is no longer added automatically).

Example — memory_usage

python

print(df.memory_usage(deep=True))

text

Index          132
player         499
team           413
matches         64
runs            64
strike_rate     64
dtype: int64

The inspection checklist

text

1. df.shape                 how big is it?
2. df.head() / df.sample()  does it look right?
3. df.info()                column types + missing values
4. df.describe()            ranges, outliers, suspicious min/max
5. df.isna().sum()          missing values per column (data-cleaning chapter)
6. df["col"].value_counts() categories in a text column

Common mistakes

❌ print(df.info()) → prints the report and then None. ✅ Just call df.info().
❌ Assuming describe() covers all columns. ✅ It is numeric-only by default; use include="all".
❌ Judging a sorted dataset by head() alone. ✅ Also check tail() and sample().
❌ Ignoring a numeric-looking column with dtype str in info(). ✅ Convert it (see "Data Types & astype").

Interview tip

Key points to remember

head(n)/tail(n) show the first/last rows; sample(n, random_state=…) shows random rows.
info() prints row count, column names, non-null counts, dtypes and memory, and returns None.
Compare each column's non-null count with the row count to spot missing values.
describe() summarises numeric columns: count, mean, std, min, 25%, 50%, 75%, max.
describe(include='all') adds text columns with count, unique, top and freq.
In pandas 3 use include='str' (not 'object') to describe text columns.
memory_usage(deep=True) shows the real bytes used by each column.

Try it yourself

First look: head, tail, sample, info

import pandas as pd

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli",
               "Yashasvi Jaiswal", "Suryakumar Yadav", "Ruturaj Gaikwad", "Rinku Singh"],
    "team": ["GT", "RCB", "CSK", "RCB", "RR", "MI", "CSK", "KKR"],
    "matches": [17, 14, 16, 14, 14, 16, 16, 14],
    "runs": [890, 730, 672, 639, 625, 605, 590, 474],
    "strike_rate": [157.8, 153.7, 139.7, 139.8, 163.6, 181.1, 147.5, 149.5],
})
print(df.head(3))
print(df.tail(2))
print(df.sample(2, random_state=7))
df.info()

import pandas as pd

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli",
               "Yashasvi Jaiswal", "Suryakumar Yadav", "Ruturaj Gaikwad", "Rinku Singh"],
    "team": ["GT", "RCB", "CSK", "RCB", "RR", "MI", "CSK", "KKR"],
    "matches": [17, 14, 16, 14, 14, 16, 16, 14],
    "runs": [890, 730, 672, 639, 625, 605, 590, 474],
    "strike_rate": [157.8, 153.7, 139.7, 139.8, 163.6, 181.1, 147.5, 149.5],
})
print(df.head(3))
print(df.tail(2))
print(df.sample(2, random_state=7))
df.info()

describe: numbers only vs include='all'

import pandas as pd

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli",
               "Yashasvi Jaiswal", "Suryakumar Yadav", "Ruturaj Gaikwad", "Rinku Singh"],
    "team": ["GT", "RCB", "CSK", "RCB", "RR", "MI", "CSK", "KKR"],
    "matches": [17, 14, 16, 14, 14, 16, 16, 14],
    "runs": [890, 730, 672, 639, 625, 605, 590, 474],
    "strike_rate": [157.8, 153.7, 139.7, 139.8, 163.6, 181.1, 147.5, 149.5],
})
print(df.describe().round(1))
print()
print(df.describe(include="str"))
print()
print(df.describe(include="all").round(1))

import pandas as pd

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli",
               "Yashasvi Jaiswal", "Suryakumar Yadav", "Ruturaj Gaikwad", "Rinku Singh"],
    "team": ["GT", "RCB", "CSK", "RCB", "RR", "MI", "CSK", "KKR"],
    "matches": [17, 14, 16, 14, 14, 16, 16, 14],
    "runs": [890, 730, 672, 639, 625, 605, 590, 474],
    "strike_rate": [157.8, 153.7, 139.7, 139.8, 163.6, 181.1, 147.5, 149.5],
})
print(df.describe().round(1))
print()
print(df.describe(include="str"))
print()
print(df.describe(include="all").round(1))

Spot missing values and memory

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli"],
    "team": ["GT", "RCB", "CSK", None],
    "runs": [890, 730, 672, 639],
    "strike_rate": [157.8, np.nan, 139.7, 139.8],
})
df.info()
print()
print(df.memory_usage(deep=True))
print("Total bytes:", df.memory_usage(deep=True).sum())

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "player": ["Shubman Gill", "Faf du Plessis", "Devon Conway", "Virat Kohli"],
    "team": ["GT", "RCB", "CSK", None],
    "runs": [890, 730, 672, 639],
    "strike_rate": [157.8, np.nan, 139.7, 139.8],
})
df.info()
print()
print(df.memory_usage(deep=True))
print("Total bytes:", df.memory_usage(deep=True).sum())

Practice: inspect a bowling table

# 🏏 Your turn: add a bowler with a missing economy (None), then Run and read info() and describe().
import pandas as pd

bowl = pd.DataFrame({
    "bowler": ["Mohammed Shami", "Mohit Sharma", "Rashid Khan", "Piyush Chawla"],
    "team": ["GT", "GT", "GT", "MI"],
    "wickets": [28, 27, 27, 22],
    "economy": [8.03, 8.17, 8.24, 8.11],
})
bowl.info()
print(bowl.describe(include="all"))

# 🏏 Your turn: add a bowler with a missing economy (None), then Run and read info() and describe().
import pandas as pd

bowl = pd.DataFrame({
    "bowler": ["Mohammed Shami", "Mohit Sharma", "Rashid Khan", "Piyush Chawla"],
    "team": ["GT", "GT", "GT", "MI"],
    "wickets": [28, 27, 27, 22],
    "economy": [8.03, 8.17, 8.24, 8.11],
})
bowl.info()
print(bowl.describe(include="all"))

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