pandas dataframe append code example

Example 1: how to adda vaslues to data frame

df = pd.DataFrame(columns=['A'])
for i in range(5):
    df = df.append({'A': i}, ignore_index=True)
df
   A
0  0
1  1
2  2
3  3
4  4

Example 2: append to pandas dataframe

df = df.append({'index1': value1, 'index2':value2,...}, ignore_index=True)

Example 3: append dataframe pandas

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
>>> df
   A  B
0  1  2
1  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
>>> df.append(df2)
   A  B
0  1  2
1  3  4
0  5  6
1  7  8

Example 4: pandas add dataframe to the bottom of another

# Basic syntax:
import pandas as pd
appended_dataframe = dataframe_1.append(dataframe_2)
# or:
appended_dataframe = pd.concat([dataframe_1, dataframe_2]) 

# Example usage:
dataframe_1 = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
dataframe_2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
appended_dataframe = dataframe_1.append(dataframe_2)
print(appended_dataframe)
   A  B
0  1  2
1  3  4
0  5  6
1  7  8

# Note, add "ignore_index = False" if you want new sequential row indices
# Note, append does not modify the dataframes in place, which is why
#	running just dataframe_1.append(dataframe_2) doesn't change
#	dataframe_1
# Note, if the column names aren't the same, the dataframes will be
#	appended with NaNs like:
     A    B    C    D
0  1.0  2.0  NaN  NaN
1  3.0  4.0  NaN  NaN
0  NaN  NaN  5.0  6.0
1  NaN  NaN  7.0  8.0

Example 5: appending dataframes pandas

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
>>> df
   A  B
0  1  2
1  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
>>> df.append(df2)
   A  B
0  1  2
1  3  4
0  5  6
1  7  8