101 python pandas exercises are designed to challenge your logical muscle and to help internalize data manipulation with python’s favorite package for data analysis. The questions are of 3 levels of difficulties with L1 being the easiest to L3 being the hardest.
101 Pandas Exercises. Photo by Chester Ho.
You might also like to practice the 101 NumPy exercises, they are often used together.
1. How to import pandas and check the version?
Show Solutionimport numpy as np # optional
import pandas as pd
print(pd.__version__)
print(pd.show_versions(as_json=True))
0.20.3
{'system': {'commit': None}, 'dependencies': {'pandas': '0.20.3', 'pytest': '3.2.1', 'pip': '9.0.1', 'setuptools': '36.5.0.post20170921', 'Cython': '0.26.1', 'numpy': '1.13.3', 'scipy': '0.19.1', 'xarray': None, 'IPython': '6.1.0', 'sphinx': '1.6.3', 'patsy': '0.4.1', 'dateutil': '2.6.1', 'pytz': '2017.2', 'blosc': None, 'bottleneck': '1.2.1', 'tables': '3.4.2', 'numexpr': '2.6.2', 'feather': None, 'matplotlib': '2.1.0', 'openpyxl': '2.4.8', 'xlrd': '1.1.0', 'xlwt': '1.2.0', 'xlsxwriter': '1.0.2', 'lxml': '4.1.0', 'bs4': '4.6.0', 'html5lib': '0.999999999', 'sqlalchemy': '1.1.13', 'pymysql': None, 'psycopg2': None, 'jinja2': '2.9.6', 's3fs': None, 'pandas_gbq': None, 'pandas_datareader': None}}
None
2. How to create a series from a list, numpy array and dict?
Create a pandas series from each of the items below: a list, numpy and a dictionary
Input
import numpy as np
mylist = list('abcedfghijklmnopqrstuvwxyz')
myarr = np.arange(26)
mydict = dict(zip(mylist, myarr))
# Inputs
import numpy as np
mylist = list('abcedfghijklmnopqrstuvwxyz')
myarr = np.arange(26)
mydict = dict(zip(mylist, myarr))
# Solution
ser1 = pd.Series(mylist)
ser2 = pd.Series(myarr)
ser3 = pd.Series(mydict)
print(ser3.head())
a 0
b 1
c 2
d 4
e 3
dtype: int64
3. How to convert the index of a series into a column of a dataframe?
Difficulty Level: L1
Convert the series ser into a dataframe with its index as another column on the dataframe.
Input
mylist = list('abcedfghijklmnopqrstuvwxyz')
myarr = np.arange(26)
mydict = dict(zip(mylist, myarr))
ser = pd.Series(mydict)
# Input
mylist = list('abcedfghijklmnopqrstuvwxyz')
myarr = np.arange(26)
mydict = dict(zip(mylist, myarr))
ser = pd.Series(mydict)
# Solution
df = ser.to_frame().reset_index()
print(df.head())
index 0
0 a 0
1 b 1
2 c 2
3 d 4
4 e 3
4. How to combine many series to form a dataframe?
Difficulty Level: L1
Combine ser1 and ser2 to form a dataframe.
Input
import numpy as np
ser1 = pd.Series(list('abcedfghijklmnopqrstuvwxyz'))
ser2 = pd.Series(np.arange(26))
# Input
import numpy as np
ser1 = pd.Series(list('abcedfghijklmnopqrstuvwxyz'))
ser2 = pd.Series(np.arange(26))
# Solution 1
df = pd.concat([ser1, ser2], axis=1)
# Solution 2
df = pd.DataFrame({'col1': ser1, 'col2': ser2})
print(df.head())
col1 col2
0 a 0
1 b 1
2 c 2
3 e 3
4 d 4
5. How to assign name to the series’ index?
Difficulty Level: L1
Give a name to the series ser calling it ‘alphabets’.
Input
ser = pd.Series(list('abcedfghijklmnopqrstuvwxyz'))
# Input
ser = pd.Series(list('abcedfghijklmnopqrstuvwxyz'))
# Solution
ser.name = 'alphabets'
ser.head()
0 a
1 b
2 c
3 e
4 d
Name: alphabets, dtype: object
6. How to get the items of series A not present in series B?
Difficulty Level: L2
From ser1 remove items present in ser2.
ser1 = pd.Series([1, 2, 3, 4, 5])
ser2 = pd.Series([4, 5, 6, 7, 8])
# Input
ser1 = pd.Series([1, 2, 3, 4, 5])
ser2 = pd.Series([4, 5, 6, 7, 8])
# Solution
ser1[~ser1.isin(ser2)]
0 1
1 2
2 3
dtype: int64
7. How to get the items not common to both series A and series B?
Difficulty Level: L2
Get all items of ser1 and ser2 not common to both.
Input
ser1 = pd.Series([1, 2, 3, 4, 5])
ser2 = pd.Series([4, 5, 6, 7, 8])
# Input
ser1 = pd.Series([1, 2, 3, 4, 5])
ser2 = pd.Series([4, 5, 6, 7, 8])
# Solution
ser_u = pd.Series(np.union1d(ser1, ser2)) # union
ser_i = pd.Series(np.intersect1d(ser1, ser2)) # intersect
ser_u[~ser_u.isin(ser_i)]
0 1
1 2
2 3
5 6
6 7
7 8
dtype: int64
8. How to get the minimum, 25th percentile, median, 75th, and max of a numeric series?
Difficuty Level: L2
Compute the minimum, 25th percentile, median, 75th, and maximum of ser.
Input
ser = pd.Series(np.random.normal(10, 5, 25))
# Input
state = np.random.RandomState(100)
ser = pd.Series(state.normal(10, 5, 25))
# Solution
np.percentile(ser, q=[0, 25, 50, 75, 100])
array([ 1.39267584, 6.49135133, 10.2578186 , 13.06985067, 25.80920994])
9. How to get frequency counts of unique items of a series?
Difficulty Level: L1
Calculte the frequency counts of each unique value ser.
Input
ser = pd.Series(np.take(list('abcdefgh'), np.random.randint(8, size=30)))
# Input
ser = pd.Series(np.take(list('abcdefgh'), np.random.randint(8, size=30)))
# Solution
ser.value_counts()
f 8
g 7
b 6
c 4
a 2
e 2
h 1
dtype: int64
10. How to keep only top 2 most frequent values as it is and replace everything else as ‘Other’?
Difficulty Level: L2
From ser, keep the top 2 most frequent items as it is and replace everything else as ‘Other’.
Input
np.random.RandomState(100)
ser = pd.Series(np.random.randint(1, 5, [12]))
# Input
np.random.RandomState(100)
ser = pd.Series(np.random.randint(1, 5, [12]))
# Solution
print("Top 2 Freq:", ser.value_counts())
ser[~ser.isin(ser.value_counts().index[:2])] = 'Other'
ser
Top 2 Freq: 4 5
3 3
2 2
1 2
dtype: int64
0 Other
1 Other
2 3
3 4
4 Other
5 4
6 4
7 3
8 3
9 4
10 4
11 Other
dtype: object
11. How to bin a numeric series to 10 groups of equal size?
Difficulty Level: L2
Bin the series ser into 10 equal deciles and replace the values with the bin name.
Input
ser = pd.Series(np.random.random(20))
Desired Output
# First 5 items
0 7th
1 9th
2 7th
3 3rd
4 8th
dtype: category
Categories (10, object): [1st < 2nd < 3rd < 4th ... 7th < 8th < 9th < 10th]
# Input
ser = pd.Series(np.random.random(20))
print(ser.head())
# Solution
pd.qcut(ser, q=[0, .10, .20, .3, .4, .5, .6, .7, .8, .9, 1],
labels=['1st', '2nd', '3rd', '4th', '5th', '6th', '7th', '8th', '9th', '10th']).head()
0 0.556912
1 0.892955
2 0.566632
3 0.146656
4 0.881579
dtype: float64
0 7th
1 9th
2 7th
3 3rd
4 8th
dtype: category
Categories (10, object): [1st < 2nd < 3rd < 4th ... 7th < 8th < 9th < 10th]
12. How to convert a numpy array to a dataframe of given shape? (L1)
Difficulty Level: L1
Reshape the series ser into a dataframe with 7 rows and 5 columns
Input
ser = pd.Series(np.random.randint(1, 10, 35))
# Input
ser = pd.Series(np.random.randint(1, 10, 35))
# Solution
df = pd.DataFrame(ser.values.reshape(7,5))
print(df)
0 1 2 3 4
0 1 2 1 2 5
1 1 2 4 5 2
2 1 3 3 2 8
3 8 6 4 9 6
4 2 1 1 8 5
5 3 2 8 5 6
6 1 5 5 4 6
13. How to find the positions of numbers that are multiples of 3 from a series?
Difficulty Level: L2
Find the positions of numbers that are multiples of 3 from ser.
Input
ser = pd.Series(np.random.randint(1, 10, 7))
# Input
ser = pd.Series(np.random.randint(1, 10, 7))
ser
# Solution
print(ser)
np.argwhere(ser % 3==0)
0 6
1 8
2 6
3 7
4 6
5 2
6 4
dtype: int64
array([[0],
[2],
[4]])
14. How to extract items at given positions from a series
Difficulty Level: L1
From ser, extract the items at positions in list pos.
Input
ser = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))
pos = [0, 4, 8, 14, 20]
# Input
ser = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))
pos = [0, 4, 8, 14, 20]
# Solution
ser.take(pos)
0 a
4 e
8 i
14 o
20 u
dtype: object
15. How to stack two series vertically and horizontally ?
Difficulty Level: L1
Stack ser1 and ser2 vertically and horizontally (to form a dataframe).
Input
ser1 = pd.Series(range(5))
ser2 = pd.Series(list('abcde'))
# Input
ser1 = pd.Series(range(5))
ser2 = pd.Series(list('abcde'))
# Output
# Vertical
ser1.append(ser2)
# Horizontal
df = pd.concat([ser1, ser2], axis=1)
print(df)
0 1
0 0 a
1 1 b
2 2 c
3 3 d
4 4 e
16. How to get the positions of items of series A in another series B?
Difficulty Level: L2
Get the positions of items of ser2 in ser1 as a list.
Input
ser1 = pd.Series([10, 9, 6, 5, 3, 1, 12, 8, 13])
ser2 = pd.Series([1, 3, 10, 13])
# Input
ser1 = pd.Series([10, 9, 6, 5, 3, 1, 12, 8, 13])
ser2 = pd.Series([1, 3, 10, 13])
# Solution 1
[np.where(i == ser1)[0].tolist()[0] for i in ser2]
# Solution 2
[pd.Index(ser1).get_loc(i) for i in ser2]
[5, 4, 0, 8]
17. How to compute the mean squared error on a truth and predicted series?
Difficulty Level: L2
Compute the mean squared error of truth and pred series.
Input
truth = pd.Series(range(10))
pred = pd.Series(range(10)) + np.random.random(10)
# Input
truth = pd.Series(range(10))
pred = pd.Series(range(10)) + np.random.random(10)
# Solution
np.mean((truth-pred)**2)
0.28448128110629545
18. How to convert the first character of each element in a series to uppercase?
Difficulty Level: L2
Change the first character of each word to upper case in each word of ser.
ser = pd.Series(['how', 'to', 'kick', 'ass?'])
# Input
ser = pd.Series(['how', 'to', 'kick', 'ass?'])
# Solution 1
ser.map(lambda x: x.title())
# Solution 2
ser.map(lambda x: x[0].upper() + x[1:])
# Solution 3
pd.Series([i.title() for i in ser])
0 How
1 To
2 Kick
3 Ass?
dtype: object
19. How to calculate the number of characters in each word in a series?
Difficulty Level: L2
Input
ser = pd.Series(['how', 'to', 'kick', 'ass?'])
# Input
ser = pd.Series(['how', 'to', 'kick', 'ass?'])
# Solution
ser.map(lambda x: len(x))
0 3
1 2
2 4
3 4
dtype: int64
20. How to compute difference of differences between consequtive numbers of a series?
Difficulty Level: L1
Difference of differences between the consequtive numbers of ser.
Input
ser = pd.Series([1, 3, 6, 10, 15, 21, 27, 35])
Desired Output
[nan, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0, 8.0]
[nan, nan, 1.0, 1.0, 1.0, 1.0, 0.0, 2.0]
# Input
ser = pd.Series([1, 3, 6, 10, 15, 21, 27, 35])
# Solution
print(ser.diff().tolist())
print(ser.diff().diff().tolist())
[nan, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0, 8.0]
[nan, nan, 1.0, 1.0, 1.0, 1.0, 0.0, 2.0]
21. How to convert a series of date-strings to a timeseries?
Difficiulty Level: L2
Input
ser = pd.Series(['01 Jan 2010', '02-02-2011', '20120303', '2013/04/04', '2014-05-05', '2015-06-06T12:20'])
Desired Output
0 2010-01-01 00:00:00
1 2011-02-02 00:00:00
2 2012-03-03 00:00:00
3 2013-04-04 00:00:00
4 2014-05-05 00:00:00
5 2015-06-06 12:20:00
dtype: datetime64[ns]
# Input
ser = pd.Series(['01 Jan 2010', '02-02-2011', '20120303', '2013/04/04', '2014-05-05', '2015-06-06T12:20'])
# Solution 1
from dateutil.parser import parse
ser.map(lambda x: parse(x))
# Solution 2
pd.to_datetime(ser)
0 2010-01-01 00:00:00
1 2011-02-02 00:00:00
2 2012-03-03 00:00:00
3 2013-04-04 00:00:00
4 2014-05-05 00:00:00
5 2015-06-06 12:20:00
dtype: datetime64[ns]
22. How to get the day of month, week number, day of year and day of week from a series of date strings?
Difficiulty Level: L2
Get the day of month, week number, day of year and day of week from ser.
Input
ser = pd.Series(['01 Jan 2010', '02-02-2011', '20120303', '2013/04/04', '2014-05-05', '2015-06-06T12:20'])
Desired output
Date: [1, 2, 3, 4, 5, 6]
Week number: [53, 5, 9, 14, 19, 23]
Day num of year: [1, 33, 63, 94, 125, 157]
Day of week: ['Friday', 'Wednesday', 'Saturday', 'Thursday', 'Monday', 'Saturday']
# Input
ser = pd.Series(['01 Jan 2010', '02-02-2011', '20120303', '2013/04/04', '2014-05-05', '2015-06-06T12:20'])
# Solution
from dateutil.parser import parse
ser_ts = ser.map(lambda x: parse(x))
# day of month
print("Date: ", ser_ts.dt.day.tolist())
# week number
print("Week number: ", ser_ts.dt.weekofyear.tolist())
# day of year
print("Day number of year: ", ser_ts.dt.dayofyear.tolist())
# day of week
print("Day of week: ", ser_ts.dt.weekday_name.tolist())
Date: [1, 2, 3, 4, 5, 6]
Week number: [53, 5, 9, 14, 19, 23]
Day num of year: [1, 33, 63, 94, 125, 157]
Day of week: ['Friday', 'Wednesday', 'Saturday', 'Thursday', 'Monday', 'Saturday']
23. How to convert year-month string to dates corresponding to the 4th day of the month?
Difficiulty Level: L2
Change ser to dates that start with 4th of the respective months.
Input
ser = pd.Series(['Jan 2010', 'Feb 2011', 'Mar 2012'])
Desired Output
0 2010-01-04
1 2011-02-04
2 2012-03-04
dtype: datetime64[ns]
import pandas as pd
# Input
ser = pd.Series(['Jan 2010', 'Feb 2011', 'Mar 2012'])
# Solution 1
from dateutil.parser import parse
# Parse the date
ser_ts = ser.map(lambda x: parse(x))
# Construct date string with date as 4
ser_datestr = ser_ts.dt.year.astype('str') + '-' + ser_ts.dt.month.astype('str') + '-' + '04'
# Format it.
[parse(i).strftime('%Y-%m-%d') for i in ser_datestr]
# Solution 2
ser.map(lambda x: parse('04 ' + x))
0 2010-01-04
1 2011-02-04
2 2012-03-04
dtype: datetime64[ns]
24. How to filter words that contain atleast 2 vowels from a series?
Difficiulty Level: L3
From ser, extract words that contain atleast 2 vowels.
Input
ser = pd.Series(['Apple', 'Orange', 'Plan', 'Python', 'Money'])
Desired Output
0 Apple
1 Orange
4 Money
dtype: object
# Input
ser = pd.Series(['Apple', 'Orange', 'Plan', 'Python', 'Money'])
# Solution
from collections import Counter
mask = ser.map(lambda x: sum([Counter(x.lower()).get(i, 0) for i in list('aeiou')]) >= 2)
ser[mask]
0 Apple
1 Orange
4 Money
dtype: object
25. How to filter valid emails from a series?
Difficiulty Level: L3
Extract the valid emails from the series emails. The regex pattern for valid emails is provided as reference.
Input
emails = pd.Series(['buying books at amazom.com', '[email protected]', '[email protected]', '[email protected]'])
pattern ='[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,4}'
Desired Output
1 [email protected]
2 [email protected]
3 [email protected]
dtype: object
# Input
emails = pd.Series(['buying books at amazom.com', '[email protected]', '[email protected]', '[email protected]'])
# Solution 1 (as series of strings)
import re
pattern ='[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,4}'
mask = emails.map(lambda x: bool(re.match(pattern, x)))
emails[mask]
# Solution 2 (as series of list)
emails.str.findall(pattern, flags=re.IGNORECASE)
# Solution 3 (as list)
[x[0] for x in [re.findall(pattern, email) for email in emails] if len(x) > 0]
['[email protected]', '[email protected]', '[email protected]']
26. How to get the mean of a series grouped by another series?
Difficiulty Level: L2
Compute the mean of weights of each fruit.
Input
fruit = pd.Series(np.random.choice(['apple', 'banana', 'carrot'], 10))
weights = pd.Series(np.linspace(1, 10, 10))
print(weight.tolist())
print(fruit.tolist())
#> [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
#> ['banana', 'carrot', 'apple', 'carrot', 'carrot', 'apple', 'banana', 'carrot', 'apple', 'carrot']
Desired output
# values can change due to randomness
apple 6.0
banana 4.0
carrot 5.8
dtype: float64
# Input
fruit = pd.Series(np.random.choice(['apple', 'banana', 'carrot'], 10))
weights = pd.Series(np.linspace(1, 10, 10))
# Solution
weights.groupby(fruit).mean()
apple 7.4
banana 2.0
carrot 6.0
dtype: float64
27. How to compute the euclidean distance between two series?
Difficiulty Level: L2
Compute the euclidean distance between series (points) p and q, without using a packaged formula.
Input
p = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
q = pd.Series([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
Desired Output
18.165
# Input
p = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
q = pd.Series([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
# Solution
sum((p - q)**2)**.5
# Solution (using func)
np.linalg.norm(p-q)
18.165902124584949
28. How to find all the local maxima (or peaks) in a numeric series?
Difficiulty Level: L3
Get the positions of peaks (values surrounded by smaller values on both sides) in ser.
Input
ser = pd.Series([2, 10, 3, 4, 9, 10, 2, 7, 3])
Desired output
array([1, 5, 7])
# Input
ser = pd.Series([2, 10, 3, 4, 9, 10, 2, 7, 3])
# Solution
dd = np.diff(np.sign(np.diff(ser)))
peak_locs = np.where(dd == -2)[0] + 1
peak_locs
array([1, 5, 7])
29. How to replace missing spaces in a string with the least frequent character?
Replace the spaces in my_str with the least frequent character.
Difficiulty Level: L2
Input
my_str = 'dbc deb abed gade'
Desired Output
'dbccdebcabedcgade' # least frequent is 'c'
# Input
my_str = 'dbc deb abed gade'
# Solution
ser = pd.Series(list('dbc deb abed gade'))
freq = ser.value_counts()
print(freq)
least_freq = freq.dropna().index[-1]
"".join(ser.replace(' ', least_freq))
d 4
b 3
e 3
3
a 2
g 1
c 1
dtype: int64
'dbccdebcabedcgade'
30. How to create a TimeSeries starting ‘2000-01-01’ and 10 weekends (saturdays) after that having random numbers as values?
Difficiulty Level: L2
Desired output
# values can be random
2000-01-01 4
2000-01-08 1
2000-01-15 8
2000-01-22 4
2000-01-29 4
2000-02-05 2
2000-02-12 4
2000-02-19 9
2000-02-26 6
2000-03-04 6
# Solution
ser = pd.Series(np.random.randint(1,10,10), pd.date_range('2000-01-01', periods=10, freq='W-SAT'))
ser
2000-01-01 6
2000-01-08 7
2000-01-15 4
2000-01-22 6
2000-01-29 8
2000-02-05 6
2000-02-12 5
2000-02-19 8
2000-02-26 1
2000-03-04 7
Freq: W-SAT, dtype: int64
31. How to fill an intermittent time series so all missing dates show up with values of previous non-missing date?
Difficiulty Level: L2
ser has missing dates and values. Make all missing dates appear and fill up with value from previous date.
Input
ser = pd.Series([1,10,3,np.nan], index=pd.to_datetime(['2000-01-01', '2000-01-03', '2000-01-06', '2000-01-08']))
print(ser)
#> 2000-01-01 1.0
#> 2000-01-03 10.0
#> 2000-01-06 3.0
#> 2000-01-08 NaN
#> dtype: float64
Desired Output
2000-01-01 1.0
2000-01-02 1.0
2000-01-03 10.0
2000-01-04 10.0
2000-01-05 10.0
2000-01-06 3.0
2000-01-07 3.0
2000-01-08 NaN
# Input
ser = pd.Series([1,10,3, np.nan], index=pd.to_datetime(['2000-01-01', '2000-01-03', '2000-01-06', '2000-01-08']))
# Solution
ser.resample('D').ffill() # fill with previous value
# Alternatives
ser.resample('D').bfill() # fill with next value
ser.resample('D').bfill().ffill() # fill next else prev value
2000-01-01 1.0
2000-01-02 10.0
2000-01-03 10.0
2000-01-04 3.0
2000-01-05 3.0
2000-01-06 3.0
2000-01-07 3.0
2000-01-08 3.0
Freq: D, dtype: float64
32. How to compute the autocorrelations of a numeric series?
Difficiulty Level: L3
Compute autocorrelations for the first 10 lags of ser. Find out which lag has the largest correlation.
Input
ser = pd.Series(np.arange(20) + np.random.normal(1, 10, 20))
Desired output
# values will change due to randomness
[0.29999999999999999, -0.11, -0.17000000000000001, 0.46000000000000002, 0.28000000000000003, -0.040000000000000001, -0.37, 0.41999999999999998, 0.47999999999999998, 0.17999999999999999]
Lag having highest correlation: 9
# Input
ser = pd.Series(np.arange(20) + np.random.normal(1, 10, 20))
# Solution
autocorrelations = [ser.autocorr(i).round(2) for i in range(11)]
print(autocorrelations[1:])
print('Lag having highest correlation: ', np.argmax(np.abs(autocorrelations[1:]))+1)
[0.29999999999999999, -0.11, -0.17000000000000001, 0.46000000000000002, 0.28000000000000003, -0.040000000000000001, -0.37, 0.41999999999999998, 0.47999999999999998, 0.17999999999999999]
Lag having highest correlation: 9
33. How to import only every nth row from a csv file to create a dataframe?
Difficiulty Level: L2
Import every 50th row of BostonHousing dataset as a dataframe.
Show Solution# Solution 1: Use chunks and for-loop
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', chunksize=50)
df2 = pd.DataFrame()
for chunk in df:
df2 = df2.append(chunk.iloc[0,:])
# Solution 2: Use chunks and list comprehension
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', chunksize=50)
df2 = pd.concat([chunk.iloc[0] for chunk in df], axis=1)
df2 = df2.transpose()
# Solution 3: Use csv reader
import csv
with open('BostonHousing.csv', 'r') as f:
reader = csv.reader(f)
out = []
for i, row in enumerate(reader):
if i%50 == 0:
out.append(row)
df2 = pd.DataFrame(out[1:], columns=out[0])
print(df2.head())
crim zn indus chas nox rm age \
0 0.21977 0.0 6.91 0 0.44799999999999995 5.602 62.0
1 0.0686 0.0 2.89 0 0.445 7.416 62.5
2 2.7339700000000002 0.0 19.58 0 0.871 5.597 94.9
3 0.0315 95.0 1.47 0 0.40299999999999997 6.975 15.3
4 0.19072999999999998 22.0 5.86 0 0.431 6.718 17.5
dis rad tax ptratio b lstat medv
0 6.0877 3 233 17.9 396.9 16.2 19.4
1 3.4952 2 276 18.0 396.9 6.19 33.2
2 1.5257 5 403 14.7 351.85 21.45 15.4
3 7.6534 3 402 17.0 396.9 4.56 34.9
4 7.8265 7 330 19.1 393.74 6.56 26.2
34. How to change column values when importing csv to a dataframe?
Difficulty Level: L2
Import the boston housing dataset, but while importing change the 'medv' (median house value) column so that values < 25 becomes ‘Low’ and > 25 becomes ‘High’.
# Solution 1: Using converter parameter
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv',
converters={'medv': lambda x: 'High' if float(x) > 25 else 'Low'})
# Solution 2: Using csv reader
import csv
with open('BostonHousing.csv', 'r') as f:
reader = csv.reader(f)
out = []
for i, row in enumerate(reader):
if i > 0:
row[13] = 'High' if float(row[13]) > 25 else 'Low'
out.append(row)
df = pd.DataFrame(out[1:], columns=out[0])
print(df.head())
crim zn indus chas nox \
0 0.00632 18.0 2.31 0 0.5379999999999999
1 0.02731 0.0 7.07 0 0.469
2 0.02729 0.0 7.07 0 0.469
3 0.032369999999999996 0.0 2.18 0 0.45799999999999996
4 0.06905 0.0 2.18 0 0.45799999999999996
rm age dis rad tax ptratio b lstat medv
0 6.575 65.2 4.09 1 296 15.3 396.9 4.98 Low
1 6.421 78.9 4.9671 2 242 17.8 396.9 9.14 Low
2 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 High
3 6.997999999999999 45.8 6.0622 3 222 18.7 394.63 2.94 High
4 7.147 54.2 6.0622 3 222 18.7 396.9 5.33 High
35. How to create a dataframe with rows as strides from a given series?
Difficiulty Level: L3
Input
L = pd.Series(range(15))
Desired Output
array([[ 0, 1, 2, 3],
[ 2, 3, 4, 5],
[ 4, 5, 6, 7],
[ 6, 7, 8, 9],
[ 8, 9, 10, 11],
[10, 11, 12, 13]])
L = pd.Series(range(15))
def gen_strides(a, stride_len=5, window_len=5):
n_strides = ((a.size-window_len)//stride_len) + 1
return np.array([a[s:(s+window_len)] for s in np.arange(0, a.size, stride_len)[:n_strides]])
gen_strides(L, stride_len=2, window_len=4)
array([[ 0, 1, 2, 3],
[ 2, 3, 4, 5],
[ 4, 5, 6, 7],
[ 6, 7, 8, 9],
[ 8, 9, 10, 11],
[10, 11, 12, 13]])
36. How to import only specified columns from a csv file?
Difficulty Level: L1
Import ‘crim’ and ‘medv’ columns of the BostonHousing dataset as a dataframe.
Show Solutiondf = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', usecols=['crim', 'medv'])
print(df.head())
crim medv
0 0.00632 24.0
1 0.02731 21.6
2 0.02729 34.7
3 0.03237 33.4
4 0.06905 36.2
37. How to get the nrows, ncolumns, datatype, summary stats of each column of a dataframe? Also get the array and list equivalent.
Difficulty Level: L2
Get the number of rows, columns, datatype and summary statistics of each column of the Cars93 dataset. Also get the numpy array and list equivalent of the dataframe.
Show Solutiondf = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# number of rows and columns
print(df.shape)
# datatypes
print(df.dtypes)
# how many columns under each dtype
print(df.get_dtype_counts())
print(df.dtypes.value_counts())
# summary statistics
df_stats = df.describe()
# numpy array
df_arr = df.values
# list
df_list = df.values.tolist()
(93, 27)
Manufacturer object
Model object
Type object
Min.Price float64
Price float64
Max.Price float64
MPG.city float64
MPG.highway float64
AirBags object
DriveTrain object
Cylinders object
EngineSize float64
Horsepower float64
RPM float64
Rev.per.mile float64
Man.trans.avail object
Fuel.tank.capacity float64
Passengers float64
Length float64
Wheelbase float64
Width float64
Turn.circle float64
Rear.seat.room float64
Luggage.room float64
Weight float64
Origin object
Make object
dtype: object
float64 18
object 9
dtype: int64
float64 18
object 9
dtype: int64
38. How to extract the row and column number of a particular cell with given criterion?
Difficulty Level: L1
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
Which manufacturer, model and type has the highest Price? What is the row and column number of the cell with the highest Price value?
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Solution
# Get Manufacturer with highest price
df.loc[df.Price == np.max(df.Price), ['Manufacturer', 'Model', 'Type']]
# Get Row and Column number
row, col = np.where(df.values == np.max(df.Price))
# Get the value
df.iat[row[0], col[0]]
df.iloc[row[0], col[0]]
# Alternates
df.at[row[0], 'Price']
df.get_value(row[0], 'Price')
# The difference between `iat` - `iloc` vs `at` - `loc` is:
# `iat` snd `iloc` accepts row and column numbers.
# Whereas `at` and `loc` accepts index and column names.
61.899999999999999
39. How to rename a specific columns in a dataframe?
Difficulty Level: L2
Rename the column Type as CarType in df and replace the ‘.’ in column names with ‘_’.
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
print(df.columns)
#> Index(['Manufacturer', 'Model', 'Type', 'Min.Price', 'Price', 'Max.Price',
#> 'MPG.city', 'MPG.highway', 'AirBags', 'DriveTrain', 'Cylinders',
#> 'EngineSize', 'Horsepower', 'RPM', 'Rev.per.mile', 'Man.trans.avail',
#> 'Fuel.tank.capacity', 'Passengers', 'Length', 'Wheelbase', 'Width',
#> 'Turn.circle', 'Rear.seat.room', 'Luggage.room', 'Weight', 'Origin',
#> 'Make'],
#> dtype='object')
Desired Solution
print(df.columns)
#> Index(['Manufacturer', 'Model', 'CarType', 'Min_Price', 'Price', 'Max_Price',
#> 'MPG_city', 'MPG_highway', 'AirBags', 'DriveTrain', 'Cylinders',
#> 'EngineSize', 'Horsepower', 'RPM', 'Rev_per_mile', 'Man_trans_avail',
#> 'Fuel_tank_capacity', 'Passengers', 'Length', 'Wheelbase', 'Width',
#> 'Turn_circle', 'Rear_seat_room', 'Luggage_room', 'Weight', 'Origin',
#> 'Make'],
#> dtype='object')
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Solution
# Step 1:
df=df.rename(columns = {'Type':'CarType'})
# or
df.columns.values[2] = "CarType"
# Step 2:
df.columns = df.columns.map(lambda x: x.replace('.', '_'))
print(df.columns)
Index(['Manufacturer', 'Model', 'CarType', 'Min_Price', 'Price', 'Max_Price',
'MPG_city', 'MPG_highway', 'AirBags', 'DriveTrain', 'Cylinders',
'EngineSize', 'Horsepower', 'RPM', 'Rev_per_mile', 'Man_trans_avail',
'Fuel_tank_capacity', 'Passengers', 'Length', 'Wheelbase', 'Width',
'Turn_circle', 'Rear_seat_room', 'Luggage_room', 'Weight', 'Origin',
'Make'],
dtype='object')
40. How to check if a dataframe has any missing values?
Difficulty Level: L1
Check if df has any missing values.
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Solution
df.isnull().values.any()
41. How to count the number of missing values in each column?
Difficulty Level: L2
Count the number of missing values in each column of df. Which column has the maximum number of missing values?
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Solution
n_missings_each_col = df.apply(lambda x: x.isnull().sum())
n_missings_each_col.argmax()
'Luggage.room'
42. How to replace missing values of multiple numeric columns with the mean?
Difficulty Level: L2
Replace missing values in Min.Price and Max.Price columns with their respective mean.
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Solution
df_out = df[['Min.Price', 'Max.Price']] = df[['Min.Price', 'Max.Price']].apply(lambda x: x.fillna(x.mean()))
print(df_out.head())
Min.Price Max.Price
0 12.900000 18.800000
1 29.200000 38.700000
2 25.900000 32.300000
3 17.118605 44.600000
4 17.118605 21.459091
43. How to use apply function on existing columns with global variables as additional arguments?
Difficulty Level: L3
In df, use apply method to replace the missing values in Min.Price with the column’s mean and those in Max.Price with the column’s median.
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Solution
d = {'Min.Price': np.nanmean, 'Max.Price': np.nanmedian}
df[['Min.Price', 'Max.Price']] = df[['Min.Price', 'Max.Price']].apply(lambda x, d: x.fillna(d[x.name](x)), args=(d, ))
44. How to select a specific column from a dataframe as a dataframe instead of a series?
Difficulty Level: L2
Get the first column (a) in df as a dataframe (rather than as a Series).
Input
df = pd.DataFrame(np.arange(20).reshape(-1, 5), columns=list('abcde'))
# Input
df = pd.DataFrame(np.arange(20).reshape(-1, 5), columns=list('abcde'))
# Solution
type(df[['a']])
type(df.loc[:, ['a']])
type(df.iloc[:, [0]])
# Alternately the following returns a Series
type(df.a)
type(df['a'])
type(df.loc[:, 'a'])
type(df.iloc[:, 1])
pandas.core.series.Series
45. How to change the order of columns of a dataframe?
Difficulty Level: L3
Actually 3 questions.
- In
df, interchange columns'a'and'c'. -
Create a generic function to interchange two columns, without hardcoding column names.
-
Sort the columns in reverse alphabetical order, that is colume
'e'first through column'a'last.
Input
df = pd.DataFrame(np.arange(20).reshape(-1, 5), columns=list('abcde'))
# Input
df = pd.DataFrame(np.arange(20).reshape(-1, 5), columns=list('abcde'))
# Solution Q1
df[list('cbade')]
# Solution Q2 - No hard coding
def switch_columns(df, col1=None, col2=None):
colnames = df.columns.tolist()
i1, i2 = colnames.index(col1), colnames.index(col2)
colnames[i2], colnames[i1] = colnames[i1], colnames[i2]
return df[colnames]
df1 = switch_columns(df, 'a', 'c')
# Solution Q3
df[sorted(df.columns)]
# or
df.sort_index(axis=1, ascending=False, inplace=True)
46. How to set the number of rows and columns displayed in the output?
Difficulty Level: L2
Change the pamdas display settings on printing the dataframe df it shows a maximum of 10 rows and 10 columns.
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Solution
pd.set_option('display.max_columns', 10)
pd.set_option('display.max_rows', 10)
# df
# Show all available options
# pd.describe_option()
47. How to format or suppress scientific notations in a pandas dataframe?
Difficulty Level: L2
Suppress scientific notations like ‘e-03’ in df and print upto 4 numbers after decimal.
Input
df = pd.DataFrame(np.random.random(4)**10, columns=['random'])
df
#> random
#> 0 3.474280e-03
#> 1 3.951517e-05
#> 2 7.469702e-02
#> 3 5.541282e-28
Desired Output
#> random
#> 0 0.0035
#> 1 0.0000
#> 2 0.0747
#> 3 0.0000
# Input
df = pd.DataFrame(np.random.random(4)**10, columns=['random'])
# Solution 1: Rounding
df.round(4)
# Solution 2: Use apply to change format
df.apply(lambda x: '%.4f' % x, axis=1)
# or
df.applymap(lambda x: '%.4f' % x)
# Solution 3: Use set_option
pd.set_option('display.float_format', lambda x: '%.4f' % x)
# Solution 4: Assign display.float_format
pd.options.display.float_format = '{:.4f}'.format
print(df)
# Reset/undo float formatting
pd.options.display.float_format = None
random
0 0.0002
1 0.5942
2 0.0000
3 0.0030
48. How to format all the values in a dataframe as percentages?
Difficulty Level: L2
Format the values in column 'random' of df as percentages.
Input
df = pd.DataFrame(np.random.random(4), columns=['random'])
df
#> random
#> 0 .689723
#> 1 .957224
#> 2 .159157
#> 3 .21082
Desired Output
#> random
#> 0 68.97%
#> 1 95.72%
#> 2 15.91%
#> 3 2.10%
# Input
df = pd.DataFrame(np.random.random(4), columns=['random'])
# Solution
out = df.style.format({
'random': '{0:.2%}'.format,
})
out
| random | |
|---|---|
| 0 | 21.66% |
| 1 | 44.90% |
| 2 | 85.69% |
| 3 | 92.12% |
49. How to filter every nth row in a dataframe?
Difficulty Level: L1
From df, filter the 'Manufacturer', 'Model' and 'Type' for every 20th row starting from 1st (row 0).
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
# Solution
print(df.iloc[::20, :][['Manufacturer', 'Model', 'Type']])
Manufacturer Model Type
0 Acura Integra Small
20 Chrysler LeBaron Compact
40 Honda Prelude Sporty
60 Mercury Cougar Midsize
80 Subaru Loyale Small
50. How to create a primary key index by combining relevant columns?
Difficulty Level: L2
In df, Replace NaNs with ‘missing’ in columns 'Manufacturer', 'Model' and 'Type' and create a index as a combination of these three columns and check if the index is a primary key.
Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv', usecols=[0,1,2,3,5])
Desired Output
Manufacturer Model Type Min.Price Max.Price
Acura_Integra_Small Acura Integra Small 12.9 18.8
missing_Legend_Midsize missing Legend Midsize 29.2 38.7
Audi_90_Compact Audi 90 Compact 25.9 32.3
Audi_100_Midsize Audi 100 Midsize NaN 44.6
BMW_535i_Midsize BMW 535i Midsize NaN NaN
# Input
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv', usecols=[0,1,2,3,5])
# Solution
df[['Manufacturer', 'Model', 'Type']] = df[['Manufacturer', 'Model', 'Type']].fillna('missing')
df.index = df.Manufacturer + '_' + df.Model + '_' + df.Type
print(df.index.is_unique)
True
51. How to get the row number of the nth largest value in a column?
Difficulty Level: L2
Find the row position of the 5th largest value of column 'a' in df.
Input
df = pd.DataFrame(np.random.randint(1, 30, 30).reshape(10,-1), columns=list('abc'))
# Input
df = pd.DataFrame(np.random.randint(1, 30, 30).reshape(10,-1), columns=list('abc'))
# Solution
n = 5
df['a'].argsort()[::-1][n]
a b c
0 27 7 25
1 8 4 20
2 1 7 17
3 24 9 17
4 21 15 9
5 21 16 20
6 19 27 25
7 12 8 20
8 11 16 28
9 24 13 4
4
52. How to find the position of the nth largest value greater than a given value?
Difficulty Level: L2
In ser, find the position of the 2nd largest value greater than the mean.
Input
ser = pd.Series(np.random.randint(1, 100, 15))
# Input
ser = pd.Series(np.random.randint(1, 100, 15))
# Solution
print('ser: ', ser.tolist(), 'mean: ', round(ser.mean()))
np.argwhere(ser > ser.mean())[1]
ser: [7, 77, 16, 86, 60, 38, 34, 36, 83, 27, 16, 52, 50, 52, 54] mean: 46
array([3])
53. How to get the last n rows of a dataframe with row sum > 100?
Difficulty Level: L2
Get the last two rows of df whose row sum is greater than 100.
df = pd.DataFrame(np.random.randint(10, 40, 60).reshape(-1, 4))
# Input
df = pd.DataFrame(np.random.randint(10, 40, 60).reshape(-1, 4))
# Solution
# print row sums
rowsums = df.apply(np.sum, axis=1)
# last two rows with row sum greater than 100
last_two_rows = df.iloc[np.where(rowsums > 100)[0][-2:], :]
54. How to find and cap outliers from a series or dataframe column?
Difficulty Level: L2
Replace all values of ser in the lower 5%ile and greater than 95%ile with respective 5th and 95th %ile value.
Input
ser = pd.Series(np.logspace(-2, 2, 30))
# Input
ser = pd.Series(np.logspace(-2, 2, 30))
# Solution
def cap_outliers(ser, low_perc, high_perc):
low, high = ser.quantile([low_perc, high_perc])
print(low_perc, '%ile: ', low, '|', high_perc, '%ile: ', high)
ser[ser < low] = low
ser[ser > high] = high
return(ser)
capped_ser = cap_outliers(ser, .05, .95)
0.05 %ile: 0.016049294077 | 0.95 %ile: 63.8766722202
55. How to reshape a dataframe to the largest possible square after removing the negative values?
Difficulty Level: L3
Reshape df to the largest possible square with negative values removed. Drop the smallest values if need be. The order of the positive numbers in the result should remain the same as the original.
Input
df = pd.DataFrame(np.random.randint(-20, 50, 100).reshape(10,-1))
# Input
df = pd.DataFrame(np.random.randint(-20, 50, 100).reshape(10,-1))
print(df)
# Solution
# Step 1: remove negative values from arr
arr = df[df > 0].values.flatten()
arr_qualified = arr[~np.isnan(arr)]
# Step 2: find side-length of largest possible square
n = int(np.floor(arr_qualified.shape[0]**.5))
# Step 3: Take top n^2 items without changing positions
top_indexes = np.argsort(arr_qualified)[::-1]
output = np.take(arr_qualified, sorted(top_indexes[:n**2])).reshape(n, -1)
print(output)
0 1 2 3 4 5 6 7 8 9
0 25 -13 17 16 0 6 22 44 10 -19
1 47 4 -1 29 -13 12 41 -13 49 42
2 20 -20 9 16 -17 -1 37 39 41 37
3 27 44 -5 5 3 -12 0 -13 23 45
4 8 27 -8 -3 48 -16 -5 40 16 10
5 12 12 41 -12 3 -17 -3 27 -15 -1
6 -9 -3 41 -13 1 0 28 33 -2 18
7 18 -14 35 5 4 14 4 44 14 34
8 1 24 26 28 -10 17 -14 14 38 17
9 13 12 5 9 -16 -7 12 -18 1 24
[[ 25. 17. 16. 6. 22. 44. 10. 47.]
[ 4. 29. 12. 41. 49. 42. 20. 9.]
[ 16. 37. 39. 41. 37. 27. 44. 5.]
[ 3. 23. 45. 8. 27. 48. 40. 16.]
[ 10. 12. 12. 41. 3. 27. 41. 28.]
[ 33. 18. 18. 35. 5. 4. 14. 4.]
[ 44. 14. 34. 24. 26. 28. 17. 14.]
[ 38. 17. 13. 12. 5. 9. 12. 24.]]
56. How to swap two rows of a dataframe?
Difficulty Level: L2
Swap rows 1 and 2 in df.
Input
df = pd.DataFrame(np.arange(25).reshape(5, -1))
# Input
df = pd.DataFrame(np.arange(25).reshape(5, -1))
# Solution
def swap_rows(df, i1, i2):
a, b = df.iloc[i1, :].copy(), df.iloc[i2, :].copy()
df.iloc[i1, :], df.iloc[i2, :] = b, a
return df
print(swap_rows(df, 1, 2))
0 1 2 3 4
0 0 1 2 3 4
1 10 11 12 13 14
2 5 6 7 8 9
3 15 16 17 18 19
4 20 21 22 23 24
57. How to reverse the rows of a dataframe?
Difficulty Level: L2
Reverse all the rows of dataframe df.
Input
df = pd.DataFrame(np.arange(25).reshape(5, -1))
# Input
df = pd.DataFrame(np.arange(25).reshape(5, -1))
# Solution 1
df.iloc[::-1, :]
# Solution 2
print(df.loc[df.index[::-1], :])
0 1 2 3 4
4 20 21 22 23 24
3 15 16 17 18 19
2 10 11 12 13 14
1 5 6 7 8 9
0 0 1 2 3 4
58. How to create one-hot encodings of a categorical variable (dummy variables)?
Difficulty Level: L2
Get one-hot encodings for column 'a' in the dataframe df and append it as columns.
Input
df = pd.DataFrame(np.arange(25).reshape(5,-1), columns=list('abcde'))
a b c d e
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
4 20 21 22 23 24
Output
0 5 10 15 20 b c d e
0 1 0 0 0 0 1 2 3 4
1 0 1 0 0 0 6 7 8 9
2 0 0 1 0 0 11 12 13 14
3 0 0 0 1 0 16 17 18 19
4 0 0 0 0 1 21 22 23 24
# Input
df = pd.DataFrame(np.arange(25).reshape(5,-1), columns=list('abcde'))
# Solution
df_onehot = pd.concat([pd.get_dummies(df['a']), df[list('bcde')]], axis=1)
print(df_onehot)
a b c d e
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
4 20 21 22 23 24
0 5 10 15 20 b c d e
0 1 0 0 0 0 1 2 3 4
1 0 1 0 0 0 6 7 8 9
2 0 0 1 0 0 11 12 13 14
3 0 0 0 1 0 16 17 18 19
4 0 0 0 0 1 21 22 23 24
59. Which column contains the highest number of row-wise maximum values?
Difficulty Level: L2
Obtain the column name with the highest number of row-wise maximum’s in df.
df = pd.DataFrame(np.random.randint(1,100, 40).reshape(10, -1))
# Input
df = pd.DataFrame(np.random.randint(1,100, 40).reshape(10, -1))
# Solution
print('Column with highest row maxes: ', df.apply(np.argmax, axis=1).value_counts().index[0])
Column with highest row maxes: 2
60. How to create a new column that contains the row number of nearest column by euclidean distance?
Create a new column such that, each row contains the row number of nearest row-record by euclidean distance.
Difficulty Level: L3
Input
df = pd.DataFrame(np.random.randint(1,100, 40).reshape(10, -1), columns=list('pqrs'), index=list('abcdefghij'))
df
# p q r s
# a 57 77 13 62
# b 68 5 92 24
# c 74 40 18 37
# d 80 17 39 60
# e 93 48 85 33
# f 69 55 8 11
# g 39 23 88 53
# h 63 28 25 61
# i 18 4 73 7
# j 79 12 45 34
Desired Output
df
# p q r s nearest_row dist
# a 57 77 13 62 i 116.0
# b 68 5 92 24 a 114.0
# c 74 40 18 37 i 91.0
# d 80 17 39 60 i 89.0
# e 93 48 85 33 i 92.0
# f 69 55 8 11 g 100.0
# g 39 23 88 53 f 100.0
# h 63 28 25 61 i 88.0
# i 18 4 73 7 a 116.0
# j 79 12 45 34 a 81.0
df = pd.DataFrame(np.random.randint(1,100, 40).reshape(10, -1), columns=list('pqrs'), index=list('abcdefghij'))
# Solution
import numpy as np
# init outputs
nearest_rows = []
nearest_distance = []
# iterate rows.
for i, row in df.iterrows():
curr = row
rest = df.drop(i)
e_dists = {} # init dict to store euclidean dists for current row.
# iterate rest of rows for current row
for j, contestant in rest.iterrows():
# compute euclidean dist and update e_dists
e_dists.update({j: round(np.linalg.norm(curr.values - contestant.values))})
# update nearest row to current row and the distance value
nearest_rows.append(max(e_dists, key=e_dists.get))
nearest_distance.append(max(e_dists.values()))
df['nearest_row'] = nearest_rows
df['dist'] = nearest_distance
61. How to know the maximum possible correlation value of each column against other columns?
Difficulty Level: L2
Compute maximum possible absolute correlation value of each column against other columns in df.
Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1), columns=list('pqrstuvwxy'), index=list('abcdefgh'))
# Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1), columns=list('pqrstuvwxy'), index=list('abcdefgh'))
df
# Solution
abs_corrmat = np.abs(df.corr())
max_corr = abs_corrmat.apply(lambda x: sorted(x)[-2])
print('Maximum Correlation possible for each column: ', np.round(max_corr.tolist(), 2))
Maximum Correlation possible for each column: [ 0.91 0.57 0.55 0.71 0.53 0.26 0.91 0.71 0.69 0.71]
62. How to create a column containing the minimum by maximum of each row?
Difficulty Level: L2
Compute the minimum-by-maximum for every row of df.
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))
# Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))
# Solution 1
min_by_max = df.apply(lambda x: np.min(x)/np.max(x), axis=1)
# Solution 2
min_by_max = np.min(df, axis=1)/np.max(df, axis=1)
63. How to create a column that contains the penultimate value in each row?
Difficulty Level: L2
Create a new column 'penultimate' which has the second largest value of each row of df.
Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))
# Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))
# Solution
out = df.apply(lambda x: x.sort_values().unique()[-2], axis=1)
df['penultimate'] = out
print(df)
0 1 2 3 4 5 6 7 8 9 penultimate
0 52 69 62 7 20 69 38 10 57 17 62
1 52 94 49 63 1 90 14 76 20 84 90
2 78 37 58 7 27 41 27 26 48 51 58
3 6 39 99 36 62 90 47 25 60 84 90
4 37 36 91 93 76 69 86 95 69 6 93
5 5 54 73 61 22 29 99 27 46 24 73
6 71 65 45 9 63 46 4 93 36 18 71
7 85 7 76 46 65 97 64 52 28 80 85
64. How to normalize all columns in a dataframe?
Difficulty Level: L2
- Normalize all columns of
dfby subtracting the column mean and divide by standard deviation. - Range all columns of
dfsuch that the minimum value in each column is 0 and max is 1.
Don’t use external packages like sklearn.
Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))
# Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))
# Solution Q1
out1 = df.apply(lambda x: ((x - x.mean())/x.std()).round(2))
print('Solution Q1\n',out1)
# Solution Q2
out2 = df.apply(lambda x: ((x.max() - x)/(x.max() - x.min())).round(2))
print('Solution Q2\n', out2)
Solution Q1
0 1 2 3 4 5 6 7 8 9
0 1.09 0.64 -0.33 -0.96 -1.30 0.06 0.38 1.18 -1.60 1.66
1 -0.93 -2.36 0.87 1.47 -1.15 1.27 0.07 -0.87 -0.18 0.23
2 1.53 0.48 -0.90 0.18 -0.33 0.81 -1.29 0.34 0.06 -0.55
3 0.59 -0.24 -1.06 0.61 1.18 -1.23 -0.53 -0.45 0.34 -1.25
4 0.18 0.33 1.07 1.17 0.50 -0.26 -0.25 -1.45 1.11 1.11
5 -1.16 0.64 -0.93 -0.59 -0.15 0.63 1.02 1.13 1.20 -0.19
6 -0.58 0.07 -0.20 -0.87 -0.22 -1.62 -1.04 0.81 -1.23 -1.04
7 -0.73 0.45 1.47 -1.02 1.47 0.34 1.65 -0.71 0.31 0.02
Solution Q2
0 1 2 3 4 5 6 7 8 9
0 0.16 0.00 0.71 0.98 1.00 0.42 0.43 0.00 1.00 0.00
1 0.91 1.00 0.24 0.00 0.95 0.00 0.54 0.78 0.49 0.49
2 0.00 0.05 0.93 0.52 0.65 0.16 1.00 0.32 0.41 0.76
3 0.35 0.29 1.00 0.35 0.10 0.86 0.74 0.62 0.31 1.00
4 0.50 0.10 0.16 0.12 0.35 0.53 0.65 1.00 0.03 0.19
5 1.00 0.00 0.95 0.83 0.58 0.22 0.22 0.02 0.00 0.64
6 0.78 0.19 0.66 0.94 0.61 1.00 0.91 0.14 0.87 0.93
7 0.84 0.06 0.00 1.00 0.00 0.32 0.00 0.72 0.32 0.56
65. How to compute the correlation of each row with the suceeding row?
Difficulty Level: L2
Compute the correlation of each row of df with its succeeding row.
Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))
# Input
df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, -1))
# Solution
[df.iloc[i].corr(df.iloc[i+1]).round(2) for i in range(df.shape[0])[:-1]]
0 1 2 3 4 5 6 7 8 9
0 93 49 26 2 96 56 11 73 90 65
1 54 17 47 52 65 9 21 87 94 4
2 51 11 44 77 37 57 17 25 95 26
3 84 8 61 43 63 63 59 65 69 29
4 8 27 53 95 10 35 16 61 39 83
5 30 70 91 26 12 44 37 71 21 48
6 66 44 47 44 29 99 86 78 31 1
7 17 40 28 12 89 95 79 54 81 47
[0.40999999999999998,
0.47999999999999998,
0.42999999999999999,
-0.37,
0.23000000000000001,
0.14000000000000001,
0.22]
66. How to replace both the diagonals of dataframe with 0?
Difficulty Level: L2
Replace both values in both diagonals of df with 0.
Input
df = pd.DataFrame(np.random.randint(1,100, 100).reshape(10, -1))
df
# 0 1 2 3 4 5 6 7 8 9
# 0 11 46 26 44 11 62 18 70 68 26
# 1 87 71 52 50 81 43 83 39 3 59
# 2 47 76 93 77 73 2 2 16 14 26
# 3 64 18 74 22 16 37 60 8 66 39
# 4 10 18 39 98 25 8 32 6 3 29
# 5 29 91 27 86 23 84 28 31 97 10
# 6 37 71 70 65 4 72 82 89 12 97
# 7 65 22 97 75 17 10 43 78 12 77
# 8 47 57 96 55 17 83 61 85 26 86
# 9 76 80 28 45 77 12 67 80 7 63
Desired output
# 0 1 2 3 4 5 6 7 8 9
# 0 0 46 26 44 11 62 18 70 68 0
# 1 87 0 52 50 81 43 83 39 0 59
# 2 47 76 0 77 73 2 2 0 14 26
# 3 64 18 74 0 16 37 0 8 66 39
# 4 10 18 39 98 0 0 32 6 3 29
# 5 29 91 27 86 0 0 28 31 97 10
# 6 37 71 70 0 4 72 0 89 12 97
# 7 65 22 0 75 17 10 43 0 12 77
# 8 47 0 96 55 17 83 61 85 0 86
# 9 0 80 28 45 77 12 67 80 7 0
# Input
df = pd.DataFrame(np.random.randint(1,100, 100).reshape(10, -1))
# Solution
for i in range(df.shape[0]):
df.iat[i, i] = 0
df.iat[df.shape[0]-i-1, i] = 0
67. How to get the particular group of a groupby dataframe by key?
Difficulty Level: L2
This is a question related to understanding of grouped dataframe. From df_grouped, get the group belonging to 'apple' as a dataframe.
Input
df = pd.DataFrame({'col1': ['apple', 'banana', 'orange'] * 3,
'col2': np.random.rand(9),
'col3': np.random.randint(0, 15, 9)})
df_grouped = df.groupby(['col1'])
# Input
df = pd.DataFrame({'col1': ['apple', 'banana', 'orange'] * 3,
'col2': np.random.rand(9),
'col3': np.random.randint(0, 15, 9)})
df_grouped = df.groupby(['col1'])
# Solution 1
df_grouped.get_group('apple')
# Solution 2
for i, dff in df_grouped:
if i == 'apple':
print(dff)
col1 col2 col3
0 apple 0.673434 7
3 apple 0.182348 14
6 apple 0.050457 3
68. How to get the n’th largest value of a column when grouped by another column?
Difficulty Level: L2
In df, find the second largest value of 'taste' for 'banana'
Input
df = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,
'rating': np.random.rand(9),
'price': np.random.randint(0, 15, 9)})
# Input
df = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,
'taste': np.random.rand(9),
'price': np.random.randint(0, 15, 9)})
print(df)
# Solution
df_grpd = df['taste'].groupby(df.fruit)
df_grpd.get_group('banana').sort_values().iloc[-2]
fruit price taste
0 apple 7 0.190229
1 banana 2 0.438063
2 orange 1 0.860182
3 apple 6 0.042149
4 banana 2 0.896021
5 orange 5 0.255107
6 apple 6 0.874533
7 banana 4 0.696274
8 orange 9 0.140713
0.69627423645996078
69. How to compute grouped mean on pandas dataframe and keep the grouped column as another column (not index)?
Difficulty Level: L1
In df, Compute the mean price of every fruit, while keeping the fruit as another column instead of an index.
Input
df = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,
'rating': np.random.rand(9),
'price': np.random.randint(0, 15, 9)})
# Input
df = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,
'rating': np.random.rand(9),
'price': np.random.randint(0, 15, 9)})
# Solution
out = df.groupby('fruit', as_index=False)['price'].mean()
print(out)
fruit price
0 apple 11.000000
1 banana 6.333333
2 orange 6.333333
70. How to join two dataframes by 2 columns so they have only the common rows?
Difficulty Level: L2
Join dataframes df1 and df2 by ‘fruit-pazham’ and ‘weight-kilo’.
Input
df1 = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,
'weight': ['high', 'medium', 'low'] * 3,
'price': np.random.randint(0, 15, 9)})
df2 = pd.DataFrame({'pazham': ['apple', 'orange', 'pine'] * 2,
'kilo': ['high', 'low'] * 3,
'price': np.random.randint(0, 15, 6)})
# Input
df1 = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,
'weight': ['high', 'medium', 'low'] * 3,
'price': np.random.randint(0, 15, 9)})
df2 = pd.DataFrame({'pazham': ['apple', 'orange', 'pine'] * 2,
'kilo': ['high', 'low'] * 3,
'price': np.random.randint(0, 15, 6)})
# Solution
pd.merge(df1, df2, how='inner', left_on=['fruit', 'weight'], right_on=['pazham', 'pounds'], suffixes=['_left', '_right'])
| fruit | price_left | weight | pazham | pounds | price_right | |
|---|---|---|---|---|---|---|
| 0 | apple | 5 | high | apple | high | 11 |
| 1 | apple | 10 | high | apple | high | 11 |
| 2 | apple | 8 | high | apple | high | 11 |
| 3 | orange | 6 | low | orange | low | 6 |
| 4 | orange | 7 | low | orange | low | 6 |
| 5 | orange | 0 | low | orange | low | 6 |
71. How to remove rows from a dataframe that are present in another dataframe?
Difficulty Level: L3
From df1, remove the rows that are present in df2. All three columns must be the same.
Input
df1 = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,
'weight': ['high', 'medium', 'low'] * 3,
'price': np.random.randint(0, 15, 9)})
df2 = pd.DataFrame({'pazham': ['apple', 'orange', 'pine'] * 2,
'kilo': ['high', 'low'] * 3,
'price': np.random.randint(0, 15, 6)})
# Input
df1 = pd.DataFrame({'fruit': ['apple', 'orange', 'banana'] * 3,
'weight': ['high', 'medium', 'low'] * 3,
'price': np.arange(9)})
df2 = pd.DataFrame({'fruit': ['apple', 'orange', 'pine'] * 2,
'weight': ['high', 'medium'] * 3,
'price': np.arange(6)})
# Solution
print(df1[~df1.isin(df2).all(1)])
fruit price weight
2 banana 2 low
3 apple 3 high
4 orange 4 medium
5 banana 5 low
6 apple 6 high
7 orange 7 medium
8 banana 8 low
72. How to get the positions where values of two columns match?
Difficulty Level: L2
Show Solution# Input
df = pd.DataFrame({'fruit1': np.random.choice(['apple', 'orange', 'banana'], 10),
'fruit2': np.random.choice(['apple', 'orange', 'banana'], 10)})
# Solution
np.where(df.fruit1 == df.fruit2)
(array([1, 5, 9]),)
73. How to create lags and leads of a column in a dataframe?
Difficulty Level: L2
Create two new columns in df, one of which is a lag1 (shift column a down by 1 row) of column ‘a’ and the other is a lead1 (shift column b up by 1 row).
Input
df = pd.DataFrame(np.random.randint(1, 100, 20).reshape(-1, 4), columns = list('abcd'))
a b c d
0 66 34 76 47
1 20 86 10 81
2 75 73 51 28
3 1 1 9 83
4 30 47 67 4
Desired Output
a b c d a_lag1 b_lead1
0 66 34 76 47 NaN 86.0
1 20 86 10 81 66.0 73.0
2 75 73 51 28 20.0 1.0
3 1 1 9 83 75.0 47.0
4 30 47 67 4 1.0 NaN
# Input
df = pd.DataFrame(np.random.randint(1, 100, 20).reshape(-1, 4), columns = list('abcd'))
# Solution
df['a_lag1'] = df['a'].shift(1)
df['b_lead1'] = df['b'].shift(-1)
print(df)
a b c d a_lag1 b_lead1
0 29 90 43 24 NaN 36.0
1 94 36 67 66 29.0 76.0
2 81 76 44 49 94.0 97.0
3 55 97 10 74 81.0 43.0
4 32 43 62 62 55.0 NaN
74. How to get the frequency of unique values in the entire dataframe?
Difficulty Level: L2
Get the frequency of unique values in the entire dataframe df.
Input
df = pd.DataFrame(np.random.randint(1, 10, 20).reshape(-1, 4), columns = list('abcd'))
# Input
df = pd.DataFrame(np.random.randint(1, 10, 20).reshape(-1, 4), columns = list('abcd'))
# Solution
pd.value_counts(df.values.ravel())
5 4
4 4
9 3
8 2
7 2
3 2
6 1
2 1
1 1
dtype: int64
75. How to split a text column into two separate columns?
Difficulty Level: L2
Split the string column in df to form a dataframe with 3 columns as shown.
Input
df = pd.DataFrame(["STD, City State",
"33, Kolkata West Bengal",
"44, Chennai Tamil Nadu",
"40, Hyderabad Telengana",
"80, Bangalore Karnataka"], columns=['row'])
print(df)
#> row
#> 0 STD, City\tState
#> 1 33, Kolkata\tWest Bengal
#> 2 44, Chennai\tTamil Nadu
#> 3 40, Hyderabad\tTelengana
#> 4 80, Bangalore\tKarnataka
Desired Output
0 STD City State
1 33 Kolkata West Bengal
2 44 Chennai Tamil Nadu
3 40 Hyderabad Telengana
4 80 Bangalore Karnataka
# Input
df = pd.DataFrame(["STD, City State",
"33, Kolkata West Bengal",
"44, Chennai Tamil Nadu",
"40, Hyderabad Telengana",
"80, Bangalore Karnataka"], columns=['row'])
# Solution
df_out = df.row.str.split(',|\t', expand=True)
# Make first row as header
new_header = df_out.iloc[0]
df_out = df_out[1:]
df_out.columns = new_header
print(df_out)
0 STD City State
1 33 Kolkata West Bengal
2 44 Chennai Tamil Nadu
3 40 Hyderabad Telengana
4 80 Bangalore Karnataka
To be continued . .
