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Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.append[] function is used to append rows of other dataframe to the end of the given dataframe, returning a new dataframe object. Columns not in the original dataframes are added as new columns and the new cells are populated with NaN value.
Syntax:
Parameters:
- other : DataFrame or Series/dict-like object, or list of these
- ignore_index : If True, do not use the index labels.
- verify_integrity : If True, raise ValueError on creating index with duplicates.
- sort : Sort columns if the columns of self and other are not aligned. The default sorting is deprecated and will change to not-sorting in a future version of pandas. Explicitly pass sort=True to silence the warning and sort. Explicitly pass sort=False to silence the warning and not sort.
Return Type: appended : DataFrame
Example #1: Create two data frames and append the second to the first one.
Python3
import pandas as pd
df1 = df = pd.DataFrame[{"a":[1, 2, 3, 4],
"b":[5, 6, 7, 8]}]
df2 = pd.DataFrame[{"a":[1, 2, 3],
"b":[5, 6, 7]}]
print[df1, "\n"]
df2
Output:
Now append df2 at the end of df1.
Python3
Output:
Notice the index value of the second data frame is maintained in the appended data frame. If we do not want it to happen then we can set ignore_index=True.
Python3
df1.append[df2, ignore_index = True]
Example #2: Append dataframe of different shapes. For unequal no. of columns in the data frame, a non-existent value in one of the dataframe will be filled with NaN values.
Python3
import pandas as pd
df1 = pd.DataFrame[{"a":[1, 2, 3, 4],
"b":[5, 6, 7, 8]}]
df2 = pd.DataFrame[{"a":[1, 2, 3],
"b":[5, 6, 7],
"c":[1, 5, 4]}]
df1 = df1.append[df2, ignore_index = True]
df1
Output:
Notice, that the new cells are populated with NaN values.