Master 50 Essential Pandas Exercises to Boost Your Data Skills

Basic Operations

Import pandas as pd and display the version.

import pandas as pd
pd.__version__

Create a Series from a list.

arr = [0, 1, 2, 3, 4]
df = pd.Series(arr)  # default index starts at 0
df

Create a Series from a dictionary.

d = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5}
df = pd.Series(d)
df

Create a DataFrame from a NumPy array.

dates = pd.date_range('today', periods=6)  # index
num_arr = np.random.randn(6, 4)
columns = ['A', 'B', 'C', 'D']
df = pd.DataFrame(num_arr, index=dates, columns=columns)
df

Read a CSV file with semicolon separator and GBK encoding.

df = pd.read_csv('test.csv', encoding='gbk', sep=';')

Create a DataFrame from a dictionary and set a custom index.

import numpy as np

data = {
    'animal': ['cat','cat','snake','dog','dog','cat','snake','cat','dog','dog'],
    'age': [2.5,3,0.5,np.nan,5,2,4.5,np.nan,7,3],
    'visits': [1,3,2,3,2,3,1,1,2,1],
    'priority': ['yes','yes','no','yes','no','no','no','yes','no','no']
}
labels = ['a','b','c','d','e','f','g','h','i','j']

df = pd.DataFrame(data, index=labels)
df

Show basic information of df (row count, column names, data types).

df.info()
# alternative: df.describe()

Display the first three rows of df.

df.iloc[:3]
# alternative: df.head(3)

Select the animal and age columns.

df.loc[:, ['animal', 'age']]
# alternative: df[['animal', 'age']]

Select rows with index [3,4,8] and the animal and age columns.

df.loc[df.index[[3,4,8]], ['animal', 'age']]

Filter rows where age > 3.

df[df['age'] > 3]

Filter rows where age is missing.

df[df['age'].isnull()]

Filter rows where age is between 2 and 4 (exclusive).

df[(df['age'] > 2) & (df['age'] < 4)]
# alternative: df[df['age'].between(2,4)]

Set the age of row label 'f' to 1.5.

df.loc['f', 'age'] = 1.5

Calculate the sum of the visits column.

df['visits'].sum()

Compute the average age for each distinct animal.

df.groupby('animal')['age'].mean()

Insert a new row with label 'k' and then delete it.

# insert
df.loc['k'] = [5.5, 'dog', 'no', 2]
# delete
df = df.drop('k')
df

Count occurrences of each animal.

df['animal'].value_counts()

Sort by age descending then visits ascending.

df.sort_values(by=['age','visits'], ascending=[False, True])

Map priority values "yes"/"no" to booleans.

df['priority'] = df['priority'].map({'yes': True, 'no': False})
df

Replace "snake" with "python" in the animal column.

df['animal'] = df['animal'].replace('snake', 'python')
df

Pivot to get average age for each animal ‑ visits combination.

df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')

Advanced Operations

Remove duplicate rows based on integer column A.

df = pd.DataFrame({'A':[1,2,2,3,4,5,5,5,6,7,7]})
df1 = df.loc[df['A'].shift() != df['A']]
# alternative: df.drop_duplicates(subset='A')

Subtract the row mean from each element of a numeric DataFrame.

df = pd.DataFrame(np.random.random(size=(5,3)))
df1 = df.sub(df.mean(axis=1), axis=0)

Find the column with the smallest sum in a 5‑column DataFrame.

df = pd.DataFrame(np.random.random(size=(5,5)), columns=list('abcde'))
df.sum().idxmin()

For each value in column A, sum the top‑3 corresponding B values.

df = pd.DataFrame({'A':list('aaabbcaabcccbbc'),
                   'B':[12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})
result = df.groupby('A')['B'].nlargest(3).sum(level=0)

Group column A into bins of size 10 (0‑100) and sum B per bin.

df = pd.DataFrame({'A':[1,2,11,11,33,34,35,40,79,99],
                   'B':[1,2,11,11,33,34,35,40,79,99]})
result = df.groupby(pd.cut(df['A'], np.arange(0,101,10)))['B'].sum()

Compute distance to the nearest preceding zero in column X and store in Y.

df = pd.DataFrame({'X':[7,2,0,3,4,2,5,0,3,4]})
izero = np.r_[-1, (df['X'] == 0).to_numpy().nonzero()[0]]
idx = np.arange(len(df))
df['Y'] = idx - izero[np.searchsorted(izero-1, idx)-1]

Return the coordinates of the three largest values in a numeric DataFrame.

df = pd.DataFrame(np.random.random(size=(5,3)))
coords = df.unstack().sort_values()[-3:].index.tolist()

Replace negative values in column vals with the mean of their group.

df = pd.DataFrame({'grps':list('aaabbcaabcccbbc'),
                   'vals':[-12,345,3,1,45,14,4,-52,54,23,-235,21,57,3,87]})

def replace(group):
    mask = group < 0
    group[mask] = group[~mask].mean()
    return group

df['vals'] = df.groupby('grps')['vals'].transform(replace)

Calculate a 3‑point rolling average per group, ignoring NaN.

df = pd.DataFrame({'group':list('aabbabbbabab'),
                    'value':[1,2,3,np.nan,2,3,np.nan,1,7,3,np.nan,8]})
# numerator: sum of non‑NaN values over a 3‑point window per group
num = df.fillna(0).groupby('group')['value'].rolling(3, min_periods=1).sum()
# denominator: count of non‑NaN values over the same window
den = df.groupby('group')['value'].rolling(3, min_periods=1).count()
result = (num / den).reset_index(level=0, drop=True).sort_index()

Series and Datetime Index

Create a Series indexed by all business days of 2015 with random values.

dti = pd.date_range(start='2015-01-01', end='2015-12-31', freq='B')
s = pd.Series(np.random.rand(len(dti)), index=dti)
s.head(10)

Sum values that fall on Wednesdays.

s[s.index.weekday == 2].sum()

Compute the monthly mean.

s.resample('M').mean()

Group every four months and return the date of the maximum value.

s.groupby(pd.Grouper(freq='4M')).idxmax()

Generate the sequence of the third Thursday of each month from 2015 to 2016.

pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')

Data Cleaning

df = pd.DataFrame({
    'From_To': ['LoNDon_paris','MAdrid_miLAN','londON_StockhOlm','Budapest_PaRis','Brussels_londOn'],
    'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],
    'RecentDelays': [[23,47], [], [24,43,87], [13], [67,32]],
    'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', '12. Air France', '"Swiss Air"']
})

Interpolate missing FlightNumber values and cast to integer.

df['FlightNumber'] = df['FlightNumber'].interpolate().astype(int)

Split From_To into separate From and To columns.

temp = df.From_To.str.split('_', expand=True)
temp.columns = ['From', 'To']
df = df.join(temp).drop('From_To', axis=1)

Capitalize the first letter of From and To.

df['From'] = df['From'].str.capitalize()
df['To'] = df['To'].str.capitalize()

Extract clean airline names by removing punctuation.

df['Airline'] = df['Airline'].str.extract('([a-zA-Z\s]+)', expand=False).str.strip()

Expand the list of recent delays into separate columns delay_1, delay_2, …

delays = df['RecentDelays'].apply(pd.Series)
delays.columns = ['delay_{}'.format(n) for n in range(1, len(delays.columns)+1)]
df = df.drop('RecentDelays', axis=1).join(delays)

Hierarchical Index

letters = ['A','B','C']
numbers = list(range(4))
mi = pd.MultiIndex.from_product([letters, numbers])
s = pd.Series(np.random.rand(12), index=mi)

Check whether the Series index is lexicographically sorted.

s.index.is_lexsorted()
# alternative: s.index.lexsort_depth == s.index.nlevels

Select rows where the second level index is 1 or 3.

s.loc[:, [1,3]]

Slice the Series up to level 'B' and from second‑level index 2 onward.

s.loc[pd.IndexSlice[:'B', 2:]]
# alternative: s.loc[slice(None, 'B'), slice(2, None)]

Compute the sum for each top‑level label (A, B, C).

s.sum(level=0)
# alternative: s.unstack().sum(axis=0)

Swap index levels, verify sorting, and sort if necessary.

new_s = s.swaplevel(0,1)
new_s.index.is_lexsorted()
new_s = new_s.sort_index()

Visualization

import matplotlib.pyplot as plt
df = pd.DataFrame({"xs":[1,5,2,8,1], "ys":[4,2,1,9,6]})
plt.style.use('ggplot')

Scatter plot of the DataFrame.

df.plot.scatter("xs", "ys", color="black", marker="x")

Scatter plot with size and colour encoding.

df = pd.DataFrame({
    "productivity":[5,2,3,1,4,5,6,7,8,3,4,8,9],
    "hours_in":[1,9,6,5,3,9,2,9,1,7,4,2,2],
    "happiness":[2,1,3,2,3,1,2,3,1,2,2,1,3],
    "caffienated":[0,0,1,1,0,0,0,0,1,1,0,1,0]
})
df.plot.scatter("hours_in", "productivity", s=df.happiness*100, c=df.caffienated)

Combine a bar chart (revenue) and a line chart (advertising) sharing the X‑axis but with a secondary Y‑axis.

df = pd.DataFrame({
    "revenue":[57,68,63,71,72,90,80,62,59,51,47,52],
    "advertising":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],
    "month":range(12)
})
ax = df.plot.bar("month", "revenue", color="green")
df.plot.line("month", "advertising", secondary_y=True, ax=ax)
ax.set_xlim((-1,12))