Introduction:

Numpy and Pandas are two of the most popular libraries in Python for data analysis and manipulation. While Numpy is a library for mathematical operations on arrays, Pandas provides data structures and functions to handle tabular data. Often, we need to convert Numpy arrays to Pandas dataframes, or add Numpy arrays to existing Pandas dataframes, for further analysis and manipulation.

In this article, we will explore different ways to add Numpy arrays to a Pandas dataframe, along with code examples.

Method 1: Using the "pd.DataFrame()" constructor:

The most straightforward way to convert a Numpy array to a Pandas dataframe is by using the "pd.DataFrame()" constructor. You can pass the Numpy array as the argument to this constructor and it will return a Pandas dataframe with the Numpy array as its data. Here's an example:

```
import numpy as np
import pandas as pd
# Create a Numpy array
array = np.array([[1, 2, 3], [4, 5, 6]])
# Convert Numpy array to Pandas dataframe
df = pd.DataFrame(array, columns=['A', 'B', 'C'])
print(df)
# Output:
# A B C
# 0 1 2 3
# 1 4 5 6
```

Method 2: Using the "pd.concat()" function:

Another way to add a Numpy array to a Pandas dataframe is by using the "pd.concat()" function. This function can be used to concatenate two or more dataframes. In this case, you can create a new Pandas dataframe from the Numpy array, and then concatenate it with the existing dataframe. Here's an example:

```
import numpy as np
import pandas as pd
# Create a Pandas dataframe
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# Create a Numpy array
array = np.array([[7, 8, 9], [10, 11, 12]])
# Convert Numpy array to Pandas dataframe
df2 = pd.DataFrame(array, columns=['A', 'B', 'C'])
# Concatenate the two dataframes
df = pd.concat([df1, df2])
print(df)
# Output:
# A B C
# 0 1 4 NaN
# 1 2 5 NaN
# 2 3 6 NaN
# 0 7 8 9.0
# 1 10 11 12.0
```

Note that in this case, the Numpy array has an additional column, 'C', which is not present in the original dataframe. As a result, NaN values are added to this column in the concatenated dataframe.

Method 3: Using the "pd.DataFrame.from_records()" method:

You can also convert a Numpy array to a Pandas dataframe by using the "pd.DataFrame.from_records()" method. This method converts a Numpy array to a Pandas dataframe, where each row of the Numpy array corresponds to a row in the Pandas data

Appending Numpy arrays to an existing Pandas dataframe:

In some cases, you might want to append a Numpy array to an existing Pandas dataframe, rather than concatenating it. To do this, you can use the "pd.DataFrame.append()" method. This method adds the rows from a given dataframe to the end of the existing dataframe. Here's an example:

```
import numpy as np
import pandas as pd
# Create a Pandas dataframe
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# Create a Numpy array
array = np.array([[7, 8], [10, 11]])
# Convert Numpy array to Pandas dataframe
df2 = pd.DataFrame(array, columns=['A', 'B'])
# Append the Numpy array to the Pandas dataframe
df = df.append(df2, ignore_index=True)
print(df)
# Output:
# A B
# 0 1 4
# 1 2 5
# 2 3 6
# 3 7 8
# 4 10 11
```

Note that in this case, we have set the "ignore_index" argument to "True", which resets the index of the resulting dataframe.

Adding a Numpy array as a new column to a Pandas dataframe:

Another way to add a Numpy array to a Pandas dataframe is by adding it as a new column. You can do this by assigning the Numpy array to a new column in the dataframe, using square bracket notation. Here's an example:

```
import numpy as np
import pandas as pd
# Create a Pandas dataframe
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# Create a Numpy array
array = np.array([7, 8, 9])
# Add the Numpy array as a new column to the Pandas dataframe
df['C'] = array
print(df)
# Output:
# A B C
# 0 1 4 7
# 1 2 5 8
# 2 3 6 9
```

Conclusion:

In this article, we have discussed three different ways to add Numpy arrays to Pandas dataframes, including converting the Numpy array to a Pandas dataframe, concatenating it with an existing dataframe, appending it to an existing dataframe, and adding it as a new column. These methods can be used in different scenarios, depending on your specific requirements. With the techniques discussed in this article, you should be able to handle Numpy arrays and Pandas dataframes with ease.

## Popular questions

- How do you convert a Numpy array to a Pandas dataframe?

You can convert a Numpy array to a Pandas dataframe using the "pd.DataFrame()" constructor. The Numpy array should be passed as the first argument to the constructor, and you can specify the column names using the "columns" argument. Here's an example:

```
import numpy as np
import pandas as pd
# Create a Numpy array
array = np.array([[1, 2, 3], [4, 5, 6]])
# Convert Numpy array to Pandas dataframe
df = pd.DataFrame(array, columns=['A', 'B', 'C'])
print(df)
# Output:
# A B C
# 0 1 2 3
# 1 4 5 6
```

- How do you concatenate a Numpy array with a Pandas dataframe?

You can concatenate a Numpy array with a Pandas dataframe using the "pd.concat()" function. You should first convert the Numpy array to a Pandas dataframe, and then pass both dataframes to the "pd.concat()" function. Here's an example:

```
import numpy as np
import pandas as pd
# Create a Pandas dataframe
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# Create a Numpy array
array = np.array([[7, 8, 9], [10, 11, 12]])
# Convert Numpy array to Pandas dataframe
df2 = pd.DataFrame(array, columns=['A', 'B', 'C'])
# Concatenate the Numpy array with the Pandas dataframe
df = pd.concat([df, df2], axis=0)
print(df)
# Output:
# A B C
# 0 1 4 NaN
# 1 2 5 NaN
# 2 3 6 NaN
# 0 7 8 9.0
# 1 10 11 12.0
```

- How do you append a Numpy array to a Pandas dataframe?

You can append a Numpy array to a Pandas dataframe using the "pd.DataFrame.append()" method. You should first convert the Numpy array to a Pandas dataframe, and then use the "append()" method to add the rows from the second dataframe to the end of the first dataframe. Here's an example:

```
import numpy as np
import pandas as pd
# Create a Pandas dataframe
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# Create a Numpy array
array = np.array([[7, 8], [10, 11]])
# Convert Numpy array to Pandas dataframe
df2 = pd.DataFrame(array, columns=['A', 'B'])
# Append the Numpy array to the Pandas dataframe
df = df.append(df2, ignore_index=True)
print(df)
# Output:
### Tag
Conversion
```