Get New pandas DataFrame from Existing Data in Python (2 Examples)

import pandas as pd                    # Import pandas

import pandas as pd # Import pandas

df = pd.DataFrame({'A':range(1, 7),    # Construct example pandas DataFrame
                     'B':['a', 'x', 'c', 'x', 'e', 'x'],
                     'C':[5, 9, 7, 8, 1, 1]})
print(df)
#    A  B  C
# 0  1  a  5
# 1  2  x  9
# 2  3  c  7
# 3  4  x  8
# 4  5  e  1
# 5  6  x  1

df = pd.DataFrame({'A':range(1, 7), # Construct example pandas DataFrame 'B':['a', 'x', 'c', 'x', 'e', 'x'], 'C':[5, 9, 7, 8, 1, 1]}) print(df) # A B C # 0 1 a 5 # 1 2 x 9 # 2 3 c 7 # 3 4 x 8 # 4 5 e 1 # 5 6 x 1

df_A = df.copy()                       # Create duplicate of pandas Data Frame
print(df_A)                            # Print copy
#    A  B  C
# 0  1  a  5
# 1  2  x  9
# 2  3  c  7
# 3  4  x  8
# 4  5  e  1
# 5  6  x  1

df_A = df.copy() # Create duplicate of pandas Data Frame print(df_A) # Print copy # A B C # 0 1 a 5 # 1 2 x 9 # 2 3 c 7 # 3 4 x 8 # 4 5 e 1 # 5 6 x 1

df_B = df[['A', 'C']].copy()           # Get certain columns
print(df_B)
#    A  C
# 0  1  5
# 1  2  9
# 2  3  7
# 3  4  8
# 4  5  1
# 5  6  1

df_B = df[['A', 'C']].copy() # Get certain columns print(df_B) # A C # 0 1 5 # 1 2 9 # 2 3 7 # 3 4 8 # 4 5 1 # 5 6 1