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Python practice problems help beginners to become experts in a python programming language with a small amount of practice effort.
Write a pandas program to count NaN values.
2
isna().sum().sum().import numpy as npimport pandas as pddf = pd.DataFrame({"name": ["Alex", "John", "Peter", "Klaus"], "age": [30, np.nan, 29, np.nan]})print(df.isna().sum().sum())2
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Write a pandas program to replace NaN in a column with a fixed value.
name age 0 Alex 30.0 1 John 0.0 2 Peter 29.0 3 Klaus 0.0
fillna(value).import numpy as npimport pandas as pddf = pd.DataFrame({"name": ["Alex", "John", "Peter", "Klaus"], "age": [30, np.nan, 29, np.nan]})df["age"] = df["age"].fillna(0)print(df)name age 0 Alex 30.0 1 John 0.0 2 Peter 29.0 3 Klaus 0.0
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Write a pandas program to total the population per country from a data set.
country China 46700000 India 40300000 USA 980000 Name: population, dtype: int64
groupby("country")["population"].sum().import pandas as pddata = pd.DataFrame({ "country": ["India", "China", "India", "USA", "China"], "city": ["Pune", "Shanghai", "Delhi", "Austin", "Beijing"], "population": [7_400_000, 24_900_000, 32_900_000, 980_000, 21_800_000],})print(data.groupby("country")["population"].sum().sort_values(ascending=False))country China 46700000 India 40300000 USA 980000 Name: population, dtype: int64
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Write a pandas program to write a DataFrame to a CSV file with a tab separator.
name age salary Alex 30 50000 John 27 65000
to_csv(sep="\t").import pandas as pddf = pd.DataFrame({"name": ["Alex", "John"], "age": [30, 27], "salary": [50000, 65000]})df.to_csv("people.tsv", sep="\t", index=False)print(open("people.tsv").read())name age salary Alex 30 50000 John 27 65000
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Write a pandas program to change the order of columns.
salary name age 0 50000 Alex 30 1 65000 John 27
import pandas as pddf = pd.DataFrame({"name": ["Alex", "John"], "age": [30, 27], "salary": [50000, 65000]})print(df[["salary", "name", "age"]])salary name age 0 50000 Alex 30 1 65000 John 27
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Write a pandas program to rename columns C1, C2, C3 to A, B, C.
A B C 0 1 6 8 1 3 8 2 2 9 0 6
rename(columns={...}).import pandas as pddf = pd.DataFrame({"C1": [1, 3, 9], "C2": [6, 8, 0], "C3": [8, 2, 6]})print(df.rename(columns={"C1": "A", "C2": "B", "C3": "C"}))A B C 0 1 6 8 1 3 8 2 2 9 0 6
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Write a pandas program to get a list of column headers.
['name', 'sport']
df.columns.tolist().import pandas as pddf = pd.DataFrame({"name": ["Virat", "Messi", "Kobe"], "sport": ["cricket", "football", "basketball"]})print(df.columns.tolist())['name', 'sport']
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Write a pandas program to iterate over the rows.
Yash 78 Rakesh 80 Suresh 60
itertuples() is fast and gives named fields.import pandas as pddf = pd.DataFrame([{"name": "Yash", "percentage": 78}, {"name": "Rakesh", "percentage": 80}, {"name": "Suresh", "percentage": 60}])for row in df.itertuples(index=False): print(f"{row.name:<7} {row.percentage}")Yash 78 Rakesh 80 Suresh 60
Prefer column operations over loops when you can; they are much faster on large data.
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Write a pandas program to add a salary column.
name age salary 0 Alex 30 50000 1 John 27 65000 2 Peter 29 58000 3 Klaus 33 66000
import pandas as pddf = pd.DataFrame({"name": ["Alex", "John", "Peter", "Klaus"], "age": [30, 27, 29, 33]})df["salary"] = [50000, 65000, 58000, 66000]print(df)name age salary 0 Alex 30 50000 1 John 27 65000 2 Peter 29 58000 3 Klaus 33 66000
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Write a pandas program to delete John’s record.
name age 0 Alex 30 2 Peter 29 3 Klaus 33
import pandas as pddf = pd.DataFrame({"name": ["Alex", "John", "Peter", "Klaus"], "age": [30, 27, 29, 33]})print(df[df["name"] != "John"])name age 0 Alex 30 2 Peter 29 3 Klaus 33
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