> For the complete documentation index, see [llms.txt](https://training.gitbook.io/pandas-documents/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://training.gitbook.io/pandas-documents/pandas-practice-problems.md). # Pandas Practice Problems ### Download DataSet : {% file src="/files/o1OihgdC0DnPuZhKExT3" %} 1. **`Given a dataframe containing the following columns: "Name", "Age", "City", and "Salary". How do you select all rows where the age is greater than 30 and the salary is greater than 50000?`**
Solution ```python df[(df['Age'] > 30) & (df['Salary'] > 50000)] ```
2. **`How do you calculate the mean, median, and standard deviation of the column "Salary" in the dataframe?`**
Solution ```python mean = df['Salary'].mean() median = df['Salary'].median() standard_deviation = df['Salary'].std() ```
3. **`Given a dataframe, how do you sort its values by the "Age" column in descending order?`**
Solution ```python df.dropna(axis=0, how='any') ```
5. **`How do you group the dataframe by the "City" column and calculate the mean of the "Salary" column for each city?`**
Solution ```python df.groupby('City')['Salary'].mean() ```
6. **`Given a dataframe containing the following columns: "Name", "Age", "City", and "Salary". How do you select all rows where the name starts with 'J'?`**
Solution ```python df[df['Name'].str.startswith('J')] ```
8. **`How do you drop all duplicate rows in a dataframe?`**
Solution ```python df.drop_duplicates(keep='first', inplace=True) ```
9.**`How do you rename the columns in a dataframe?`**
Solution ```python df.rename(columns={'Old_Column_Name':'New_Column_Name'}, inplace=True) ```
10. **`How do you count the number of unique values in a column?`**
Solution ```python df['City'].nunique() ```
11. **`How do you merge two dataframes on a specific column?`**
Solution ```python merged_df = pd.merge(df1, df2, on='Column_Name') ```
12. **`How do you create a pivot table from a dataframe?`**
Solution ```python pivot_table = df.pivot_table(index='Column1', columns='Column2', values='Column3') ```
13. **`How do you select the first 5 rows of a dataframe?`**
Solution ```python df.head(5) ```
14. **`How do you select the last 5 rows of a dataframe?`**
Solution ```python df.tail(5) ```
15. **`How do you select a specific column from a dataframe and make it into a new dataframe?`**
Solution ```python new_df = df[['Column_Name']] ```
16. **`How do you concatenate two dataframes along the rows?`**
Solution ```python concatenated_df = pd.concat([df1, df2], axis=0) ```
17. **`How do you find the maximum and minimum values of a column in a dataframe?`**
Solution ```python max_value = df['Column_Name'].max() min_value = df['Column_Name'].min() ```
18. **`How do you calculate the cumulative sum of a column in a dataframe?`**
Solution ```python df['Column_Name'].cumsum() ```
19. **`How do you find the unique values of a column in a dataframe?`**
Solution ```python df['Column_Name'].unique() ```
20. **`How do you drop a specific column from a dataframe?`**
Solution ```python df.drop('Column_Name', axis=1, inplace=True) ```
21. **`How do you find the number of missing values in each column of a dataframe?`**
Solution ```python df.isnull().sum() ```
22. **`How do you fill missing values in a specific column with a specific value?`**
Solution ```python df['Column_Name'].fillna(value, inplace=True) ```
23. **`How do you drop all rows with more than two missing values in the dataframe?`**
Solution ```python Solution: df.dropna(thresh=len(df) - 2, axis=0, inplace=True) ```
Let's Do some Data Cleaning and Analysis on the Practice Data. ### Data Cleaning : Although there are many steps to clean , but lets start with dropping duplicate rows.
Solution ```python df = df.drop_duplicates() ```
### Fill missing values : Find a way to fill missing values in Columns with Reasonable values.
Solution ```python df['Name'].fillna(df['Name'].mode()[0], inplace=True) df['Age'].fillna(df['Age'].mean(), inplace=True) df['City'].fillna(df['City'].mode()[0], inplace=True) df['Salary'].fillna(df['Salary'].mean(), inplace=True) ```
### Exploratory Data Analysis (EDA) : Generate a Statistical Summary.
Solution ```python print(df.describe()) ```
### Create histograms for each numeric column :
Solution ```python df.hist() plt.show() ```
### Create a bar chart to compare the distribution of salary across different cities :
Solution ```python grouped_data = df.groupby('City')['Salary'].mean().reset_index() plt.bar(grouped_data['City'], grouped_data['Salary']) plt.xlabel('City') plt.ylabel('Average Salary') plt.show() ```
### Group the data by city and calculate the mean salary for each city :
Solution ```python city_group = df.groupby('City') mean_salary_by_city = city_group['Salary'].mean().reset_index() ```
### Plot the mean salary for each city using a bar plot :
Solution ```python plt.bar(mean_salary_by_city['City'], mean_salary_by_city['Salary']) plt.xlabel("City") plt.ylabel("Mean Salary") plt.title("Mean Salary by City") plt.xticks(rotation=45) plt.show() ```
### Plot a scatter plot to visualize the relationship between Age and Salary :
Solution ```python plt.scatter(df['Age'], df['Salary']) plt.xlabel("Age") plt.ylabel("Salary") plt.title("Relationship between Age and Salary") plt.show() ```
### Find the highest-paid person in each city :
Solution ```python top_paid_by_city = city_group['Salary'].max().reset_index() print("The highest-paid person in each city:") print(top_paid_by_city) ```
### Find the youngest and oldest person in the data :
Solution ```python min_age = df['Age'].min() max_age = df['Age'].max() print("The youngest person in the data is", df.loc[df['Age'] == min_age, 'Name'].iloc[0], "with an age of", min_age) print("The oldest person in the data is", df.loc[df['Age'] == max_age, 'Name'].iloc[0], "with an age of", max_age) ```
## Group the data by age group and city and calculate the mean salary for each group and city `age_group = df.groupby(['Age Group', 'City']) mean_salary_by_age_group_and_city = age_group['Salary'].mean().reset_index()` ## Pivot the table to display the mean salary for each age group and city `pivot_table = pd.pivot_table(mean_salary_by_age_group_and_city, values='Salary', index='Age Group', columns='City')` `print("Mean Salary by Age Group and City:")` `print(pivot_table)` ## Plot the mean salary for each age group and city using a heatmap `sns.heatmap(pivot_table, annot=True, cmap='Blues')` `plt.xlabel("City")` `plt.ylabel("Age Group")` `plt.title("Mean Salary by Age Group and City")` `plt.show()` ## Calculate the top 5 most common names in the data `top_names = df['Name'].value_counts().head()` `print("The top 5 most common names in the data:")` `print(top_names)` ## Create a new column to indicate whether a person's age is below or above the average age `mean_age = df['Age'].mean()` `df['Age Above Average'] = np.where(df['Age']>=mean_age, 1, 0)` ## Group the data by age above average and city and calculate the median salary for each group and city `age_above_average_group = df.groupby(['Age Above Average', 'City'])` `median_salary_by_age_above_avg_and_city=age_above_average_group['Salary'].median().reset_index()` ## Pivot the table to display the median salary for each age above average group and city `pivot_table2 = pd.pivot_table(median_salary_by_age_above_average_and_city, values='Salary', index='Age Above Average', columns='City')` `print("Median Salary by Age Above Average and City:")` `print(pivot_table2)` ## Plot the median salary for each age above average group and city using a stacked bar chart `pivot_table2.plot(kind='bar', stacked=True)` `plt.xlabel("Age Above Average")` `plt.ylabel("Median Salary")` `plt.title("Median Salary by Age Above Average and City")` `plt.show()` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://training.gitbook.io/pandas-documents/pandas-practice-problems.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.