> 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()`