You can count the number of columns in a row in a pandas dataframe in Python by using the `shape`

attribute. The `shape`

attribute returns a tuple with the number of rows and columns in the dataframe. To count the number of columns, you can access the second element of the tuple.

For example, if you have a dataframe named `df`

, you can count the number of columns in a row by using `df.shape[1]`

. This will give you the number of columns in the dataframe.

## What is the difference between groupby() and pivot_table() in pandas?

The main difference between `groupby()`

and `pivot_table()`

in pandas is in the way they aggregate and display data.

`groupby()`

is a method that groups related data together based on a provided key and allows you to perform some operation on those groups. It is typically used for grouping and aggregating data based on one or more columns, and then applying some kind of aggregation function, such as sum, mean, count, etc. This method is more flexible and allows for complex groupings and aggregations.

`pivot_table()`

is a function that allows you to pivot your data, meaning that you can rearrange and summarize your data in a particular way. It is typically used to summarize and aggregate data in a tabular format, where rows represent one variable, columns represent another variable, and the values in the table are some aggregate function of a third variable. `pivot_table()`

is generally easier to use and more intuitive for simple summarizations and aggregations.

In summary, `groupby()`

is used for grouping and aggregating data, while `pivot_table()`

is used for summarizing and rearranging data in a tabular format. The choice between the two methods depends on your specific data analysis needs and the desired output format.

## How to calculate descriptive statistics in pandas?

To calculate descriptive statistics in pandas, you can use the `describe()`

method which provides a summary of the numerical columns in a DataFrame.

Here is an example of how to calculate descriptive statistics using `describe()`

:

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import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]} df = pd.DataFrame(data) # Calculate descriptive statistics summary_statistics = df.describe() print(summary_statistics) |

This will output the following summary statistics for each numerical column in the DataFrame:

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A B count 5.0 5.0 mean 3.0 30.0 std 1.581139 15.811388 min 1.0 10.0 25% 2.0 20.0 50% 3.0 30.0 75% 4.0 40.0 max 5.0 50.0 |

You can also calculate individual statistics such as mean, median, mode, standard deviation, variance, etc. using the appropriate methods in pandas.

## How to filter rows in a DataFrame in pandas?

To filter rows in a DataFrame in pandas, you can use the `loc`

or `iloc`

function along with a boolean condition. Here's how you can do it:

- Using loc:

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import pandas as pd # Creating a sample DataFrame df = pd.DataFrame({ 'A': [1, 2, 3, 4], 'B': ['foo', 'bar', 'foo', 'bar'] }) # Filtering rows where column A is greater than 2 filtered_df = df.loc[df['A'] > 2] print(filtered_df) |

- Using iloc:

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import pandas as pd # Creating a sample DataFrame df = pd.DataFrame({ 'A': [1, 2, 3, 4], 'B': ['foo', 'bar', 'foo', 'bar'] }) # Filtering rows where column A is greater than 2 filtered_df = df.iloc[(df['A'] > 2).values] print(filtered_df) |

Both methods will return a new DataFrame with only the rows that satisfy the condition. You can modify the condition to filter rows based on different criteria.

## How to import pandas in Python?

To import pandas in Python, you can use the following code:

```
1
``` |
```
import pandas as pd
``` |

This imports the pandas library and assigns it the alias 'pd' which is commonly used in Python for pandas. You can now use pandas functions and classes by calling `pd`

followed by a dot and the function or class name.