You can use pd.DataFrame() function to convert an array to a column in a Pandas dataframe. The following shows examples of how to convert array from Numpy to a column in Pandas.
Example 1: Single Column
Step 1: Using Numpy to create an array
# Create an array using Numpy
import numpy as np
x = np.repeat(['City1','City2'],5)
print(x)Output:
['City1' 'City1' 'City1' 'City1' 'City1' 'City2' 'City2' 'City2' 'City2' 'City2']
Step 2: Use pd.DataFrame() to convert the array to a column
# Use pd.DataFrame() to convert the array to a column
import pandas as pd
df_x=pd.DataFrame({'cities':x})
print(df_x)Output:
cities 0 City1 1 City1 2 City1 3 City1 4 City1 5 City2 6 City2 7 City2 8 City2 9 City2
Example 2: Multiple Columns
The following is an example of converting two arrays into two columns.
Step 1: Using Numpy to create two arrays
# Create two arrays using Numpy
x_1 = np.repeat(['City1','City2'],5)
print(x_1)
x_2 = np.tile(['store1','store2'], 5)
print(x_2)
Output:
['City1' 'City1' 'City1' 'City1' 'City1' 'City2' 'City2' 'City2' 'City2' 'City2'] ['store1' 'store2' 'store1' 'store2' 'store1' 'store2' 'store1' 'store2' 'store1' 'store2']
Step 2: Use pd.DataFrame() to convert the array to a column
df_x=pd.DataFrame({'cities':x_1, 'stores':x_2})
print(df_x)Output:
cities stores 0 City1 store1 1 City1 store2 2 City1 store1 3 City1 store2 4 City1 store1 5 City2 store2 6 City2 store1 7 City2 store2 8 City2 store1 9 City2 store2