## Converting Data Type in Pandas DataFrame

Have you ever come across a situation where you needed to convert the data type of certain columns in a Pandas DataFrame? Data type conversion is an essential step in data preprocessing and analysis, especially when dealing with large datasets.

### Method I: Using the astype() function

The `astype()`

function is an inbuilt method that converts the datatype of a series or a column to a specified datatype.

It is a simple and direct method of converting data types and can be used with any data type. Syntax: `DataFrame['Column Name'] = DataFrame['Column Name'].astype('datatype')`

Let us take an example of a Pandas DataFrame with a column called ‘Price’.

Suppose the datatype of this column is ‘object’, and we want to convert it to a ‘float’ data type for further analysis. We can do this by using the `astype()`

function:

```
import pandas as pd
data = {'Product Name': ['P1', 'P2', 'P3', 'P4', 'P5'],
'Price': ['12.5', '25.3', '42.7', '63.2', '79.1']
}
df = pd.DataFrame(data)
print(df.dtypes)
# Output:
# Product Name object
# Price object
# dtype: object
# Converting 'Price' column to float
df['Price'] = df['Price'].astype(float)
print(df.dtypes)
# Output:
# Product Name object
# Price float64
# dtype: object
```

As you can see, the `astype()`

function has converted the ‘Price’ column to a float data type.

### Method II: Using the apply() function

The `apply()`

function is a universal function in Pandas that is used to apply a function to each element or a subset of elements in a DataFrame.

It can also be used to convert data types, but it requires a little bit more effort compared to the `astype()`

function. The `apply()`

function can be very useful when dealing with complex data types or when we need to modify the data before converting it.

Let us take the same example of a Pandas DataFrame with a column called ‘Price’. This time, suppose the datatype of this column is ‘string’, and we want to convert it to an ‘int’ data type for further analysis.

We can do this by using the `apply()`

function and the numpy library:

```
import pandas as pd
import numpy as np
data = {'Product Name': ['P1', 'P2', 'P3', 'P4', 'P5'],
'Price': ['12', '25', '42', '63', '79']
}
df = pd.DataFrame(data)
print(df.dtypes)
# Output:
# Product Name object
# Price object
# dtype: object
# Converting 'Price' column to int
df['Price'] = df['Price'].apply(np.int64)
print(df.dtypes)
# Output:
# Product Name object
# Price int64
# dtype: object
```

As you can see, the `apply()`

function has converted the ‘Price’ column to an int data type.

## Using the Pandas DataFrame for Data Analysis

Now that we know how to convert data types in a Pandas DataFrame, let us explore how to use the DataFrame for data analysis.

### Example DataFrame

Suppose we have a Pandas DataFrame with some sample data on the revenue generated by a company in different cities. We can create this DataFrame using the following code:

```
import pandas as pd
data = {'City':['New York', 'Chicago', 'Houston', 'Boston', 'Phoenix'],
'Revenue':[55000, 47000, 38000, 69000, 42000],
'Employees':[150, 100, 90, 200, 80],
'Year Founded':[2010, 2000, 1995, 2005, 2015]
}
df = pd.DataFrame(data)
print(df)
# Output:
# City Revenue Employees Year Founded
# 0 New York 55000 150 2010
# 1 Chicago 47000 100 2000
# 2 Houston 38000 90 1995
# 3 Boston 69000 200 2005
# 4 Phoenix 42000 80 2015
```

### Checking Data Types in the DataFrame

Before we start analyzing the data, it is always good to check the data types of the columns. We can use the `dtypes`

attribute of the DataFrame to get the data types:

```
print(df.dtypes)
# Output:
# City object
# Revenue int64
# Employees int64
# Year Founded int64
# dtype: object
```

### Converting Data Type in DataFrame Columns

Suppose we want to convert the ‘Revenue’ and ‘Employees’ columns to float data types for further analysis. We can use the `astype()`

function to achieve this:

```
# Converting 'Revenue' and 'Employees' columns to float
df['Revenue'] = df['Revenue'].astype(float)
df['Employees'] = df['Employees'].astype(float)
print(df.dtypes)
# Output:
# City object
# Revenue float64
# Employees float64
# Year Founded int64
# dtype: object
```

Now, we can perform various operations on the DataFrame columns, such as calculating the mean, median, or standard deviation of revenue or employees. We can also perform groupby operations to analyze the data by cities or year founded.

## Conclusion

In this article, we discussed two methods of converting data types in Pandas DataFrame: the `astype()`

function and the `apply()`

function. We also explored some basic operations in Pandas DataFrame for data analysis.

Converting data types in a DataFrame is an essential step in data preprocessing and analysis, and we hope that this article has helped you in gaining a better understanding of this topic.

## Converting Float Columns to Integer Columns in Pandas

Data preprocessing is a crucial step in data analysis. This includes data cleaning, data transformation, and data type conversion.

Converting float columns to integer columns is a common task in data preprocessing, and it can be done in several ways using the Pandas library in Python. In this article, we will discuss two methods of converting float columns to integer columns in Pandas.

### Method I: Using the astype() function

The `astype()`

function is a built-in method of Pandas that is used to convert the datatype of a series or a column to a specified datatype. When dealing with float columns, we can use this function to convert them to integers by simply using the ‘int’ data type as the argument.

Syntax: `DataFrame['Column Name'] = DataFrame['Column Name'].astype(int)`

Let us take an example of a Pandas DataFrame with a float column called ‘Price’. We want to convert this column to an integer data type for further analysis.

We can do this by using the `astype()`

function:

```
import pandas as pd
data = {'Product Name': ['P1', 'P2', 'P3', 'P4', 'P5'],
'Price': [12.5, 25.3, 42.7, 63.2, 79.1]
}
df = pd.DataFrame(data)
print(df.dtypes)
# Output:
# Product Name object
# Price float64
# dtype: object
# Converting 'Price' column to int
df['Price'] = df['Price'].astype(int)
print(df.dtypes)
# Output:
# Product Name object
# Price int64
# dtype: object
```

As you can see, the `astype()`

function has converted the ‘Price’ column to an integer data type, but it will remove the decimal from the original float value.

### Method II: Using the apply() function

The `apply()`

function can also be used to convert float columns to integer columns.

The `apply()`

function applies a given function to each element or a subset of elements in a Pandas DataFrame. In this case, we need to use the numpy library to define the function to convert float values to integer values.

```
import pandas as pd
import numpy as np
data = {'Product Name': ['P1', 'P2', 'P3', 'P4', 'P5'],
'Price': [12.5, 25.3, 42.7, 63.2, 79.1]
}
df = pd.DataFrame(data)
print(df.dtypes)
# Output:
# Product Name object
# Price float64
# dtype: object
# Applying the apply() function to convert 'Price' column to int
df['Price'] = df['Price'].apply(np.int64)
print(df.dtypes)
# Output:
# Product Name object
# Price int64
# dtype: object
```

As you can see, the `apply()`

function has also converted the ‘Price’ column to an integer data type, but it preserves the decimal and rounds off the value.

## Conclusion

Converting float columns to integer columns is a fundamental task in data preprocessing. In this article, we discussed two methods of converting float columns to integer columns in Pandas: the `astype()`

function and the `apply()`

function.

The `astype()`

function is a simple and direct method, whereas the `apply()`

function is a bit more complex but allows for further customization. By using these functions, you can easily convert float columns to integer columns in your Pandas DataFrame for further analysis.

## Takeaways

- Converting float columns to integer columns is a common task in data preprocessing.
- The
`astype()`

function and the`apply()`

function are two methods of converting float columns to integer columns in Pandas. - The
`astype()`

function is a direct method but will remove the decimal from the original float value. - The
`apply()`

function is a bit more complex but allows for further customization and preserves the decimal by rounding off the value.

Converting float columns to integer columns is a crucial step in data preprocessing and analysis. In this article, we explored two methods of converting float columns to integer columns in Pandas: the `astype()`

function and the `apply()`

function.

The `astype()`

function is a simple and direct method, while the `apply()`

function allows for further customization and rounds off the value by preserving the decimal. It is essential to make sure that the data type of the columns is accurate before performing data analysis.

By using these methods, you can easily convert float columns to integer columns in your Pandas DataFrame and conduct further analysis with accurate data.