WorksheetsPandas Practical Quiz
Total questions: 50
Worksheet time: 25mins
Section A: Creating, Reading & Writing Data — Which function is used to read a CSV file into a DataFrame?
pd.read()
pd.read_csv()
pd.load_csv()
pd.import_csv()
Section A: Creating, Reading & Writing Data — Which parameter in read_csv() specifies the column to use as index?
index
index_col
use_index
set_index
Section A: Creating, Reading & Writing Data — What does pd.DataFrame() primarily create?
A Series
A NumPy array
A 2-D labeled data structure
A dictionary
Section A: Creating, Reading & Writing Data — Which method saves a DataFrame to a CSV file?
df.write_csv()
df.save_csv()
df.to_csv()
pd.to_csv()
Section A: Creating, Reading & Writing Data — What is the default delimiter for read_csv()?
Tab
Semicolon
Space
Comma
Section A: Creating, Reading & Writing Data — What does df.head() return by default?
Last 5 rows
First 10 rows
First 5 rows
Entire DataFrame
Section A: Creating, Reading & Writing Data — Which object represents a single column of a DataFrame?
DataFrame
Series
Array
List
Section A: Creating, Reading & Writing Data — Which argument limits rows read from a CSV?
max_rows
rows
nrows
limit
Section A: Creating, Reading & Writing Data — What does df.shape return?
Number of columns
Number of rows
(rows, columns)
Column names
Section A: Creating, Reading & Writing Data — Which function reads Excel files?
pd.read_excel()
pd.read_xls()
pd.read_sheet()
pd.open_excel()
Section B: Indexing, Selecting & Assigning — Which accessor is label-based?
iloc
loc
iat
values
Section B: Indexing, Selecting & Assigning — Which accessor is integer-position based?
loc
at
iloc
labels
Section B: Indexing, Selecting & Assigning — What does df['col'] return?
DataFrame
Series
List
NumPy array
Section B: Indexing, Selecting & Assigning — How do you select multiple columns?
df['A','B']
df[['A','B']]
df('A','B')
df[A,B]
Section B: Indexing, Selecting & Assigning — Which is used for conditional row filtering?
df.filter()
Boolean indexing
df.where() only
df.select()
Section B: Indexing, Selecting & Assigning — What does df.iloc[0] return?
First column
First row
Entire DataFrame
Index only
Section B: Indexing, Selecting & Assigning — Which method sets a column as index?
df.make_index()
df.index()
df.set_index()
df.assign_index()
Section B: Indexing, Selecting & Assigning — What does df.at[row, col] access?
Multiple values
A slice
A single scalar value
Entire column
Section B: Indexing, Selecting & Assigning — Which is faster for scalar access?
loc
iloc
at / iat
values
Section B: Indexing, Selecting & Assigning — What does df.rename() do?
Renames rows/columns
Deletes columns
Sorts index
Changes datatype
Section C: Summary Functions & Maps — Which function gives statistical summary?
df.stats()
df.summary()
df.describe()
df.info()
Section C: Summary Functions & Maps — What does df.mean() compute?
Row means
Column means
Overall mean only
Index mean
Section C: Summary Functions & Maps — Which method applies a function element-wise?
apply()
map()
transform()
filter()
Section C: Summary Functions & Maps — Series.map() is used to:
Aggregate values
Sort values
Apply a function element-wise
Group data
Section C: Summary Functions & Maps — Which function gives correlation matrix?
df.corr()
df.cov()
df.compare()
df.relate()
Section C: Summary Functions & Maps — What does df.value_counts() do?
Counts columns
Counts rows
Counts unique values
Counts nulls
Section C: Summary Functions & Maps — Which function shows non-null counts and dtypes?
df.describe()
df.info()
df.head()
df.dtypes()
Section C: Summary Functions & Maps — What does df.idxmax() return?
Maximum value
Index of maximum value
Mean value
Boolean mask
Section C: Summary Functions & Maps — Which function checks duplicates?
df.isdup()
df.duplicated()
df.has_duplicates()
df.repeat()
Section C: Summary Functions & Maps — How to drop duplicates?
df.remove_duplicates()
df.drop_dups()
df.drop_duplicates()
df.unique()
Section D: Grouping & Sorting — Which function groups data?
df.cluster()
df.groupby()
df.aggregate()
df.split()
Section D: Grouping & Sorting — groupby() is usually followed by?
sort()
apply() / aggregation
filter() only
join()
Section D: Grouping & Sorting — Which is a valid aggregation?
mean()
sum()
count()
All of the above
Section D: Grouping & Sorting — What does df.sort_values() sort by default?
Index
Rows
Column values
Datatypes
Section D: Grouping & Sorting — Which sorts by index?
df.sort()
df.sort_index()
df.order()
df.reindex()
Section D: Grouping & Sorting — What does ascending=False do?
Sort ascending
Sort descending
Remove NaNs
Reverse index only
Section D: Grouping & Sorting — Which function returns group sizes?
groupby().len()
groupby().count()
groupby().size()
groupby().shape()
Section D: Grouping & Sorting — agg() is used to:
Rename columns
Apply multiple aggregations
Filter groups
Join DataFrames
Section D: Grouping & Sorting — What does reset_index() do?
Drops columns
Converts index to column
Deletes index
Renames index
Section D: Grouping & Sorting — Which returns top N rows?
df.head(n)
df.top(n)
df.limit(n)
df.take(n)
Which detects missing values?
df.isna()
df.isnan()
df.hasna()
df.checkna()
Which fills missing values?
df.fill()
df.fillna()
df.replace()
df.complete()
What does df.dropna() do?
Fills NaNs
Drops NaN rows
Converts NaNs
Replaces NaNs
Which converts column datatype?
df.astype()
df.convert()
df.type()
df.cast()
Which combines DataFrames vertically?
merge()
join()
concat(axis=0)
append_index()
Which combines on a key column?
concat()
merge()
stack()
bind()
Default join type in merge()?
Left
Right
Inner
Outer
Which reshapes data from wide to long?
pivot()
melt()
stack()
reshape()
Which reshapes long to wide?
melt()
groupby()
pivot()
explode()
Which explodes list-like column entries?
expand()
explode()
flatten()
unpack()
