Create a new dataframe from existing dataframe pandas
You can create a new DataFrame from an existing DataFrame in pandas using various methods. Here are a few examples:
Method 1: Using the copy()
method
import pandas as pd
# create an existing DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# create a new DataFrame by copying the existing one
new_df = df.copy()
print(new_df)
This will create a new DataFrame new_df
that is a copy of the original DataFrame df
.
Method 2: Using the assign()
method
import pandas as pd
# create an existing DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# create a new DataFrame by assigning a new column to the existing one
new_df = df.assign(C=[7, 8, 9])
print(new_df)
This will create a new DataFrame new_df
that is a copy of the original DataFrame df
with an additional column C
.
Method 3: Using the loc[]
method
import pandas as pd
# create an existing DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# create a new DataFrame by selecting a subset of rows and columns from the existing one
new_df = df.loc[:, ['A', 'B']]
print(new_df)
This will create a new DataFrame new_df
that is a subset of the original DataFrame df
, containing only the columns A
and B
.
Method 4: Using the concat()
method
import pandas as pd
# create an existing DataFrame
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]})
# create a new DataFrame by concatenating two DataFrames
new_df = pd.concat([df1, df2])
print(new_df)
This will create a new DataFrame new_df
that is the concatenation of two DataFrames df1
and df2
.
These are just a few examples of how you can create a new DataFrame from an existing one in pandas. The method you choose will depend on your specific use case and requirements.