Merging series of pandas dataframe into single dataframe - python-3.x

Question

I have a series of pandas data frame stored in variable df similar to below:
df
| 0 | 1 |
+-------+--------+
|ABCD | WXYZ |
| 0 | 1 |
+-------+--------+
|DEFJ | HJKL |
| 0 | 1 |
+-------+--------+
|ZXCT | WYOM |
| 0 | 1 |
+-------+--------+
|TYZX | NMEX |
I want to merge them to a single pandas data frame as below :
| 0 | 1 |
+-------+--------+
|ABCD | WXYZ |
|DEFJ | HJKL |
|ZXCT | WYOM |
|TYZX | NMEX |
So how can I merge series of pandas dataframe into one single pandas dataframe ?

Answer 1

As your code is now, you're only outputing one dataframe with one row only (overwriting the others).
Try this:
# Copy the names to pandas dataframes and save them in a list
import pandas as pd
dfs = []
for j in range(0,5):
for i in divs[j].find_elements_by_tag_name('a'):
i = i.get_attribute('text')
i = parse_name(i)
df = pd.DataFrame(i)
df = df.transpose()
dfs.append(df)
# Aggregate all dataframes in one
new_df = dfs[0]
for df in dfs[1:]:
new_df = new_df.append(df)
# Update index
new_df = new_df.reset_index(drop=True)
# Print first five rows
new_df.head()
0 1
0 Lynn Batten Emeritus Professor
1 Andrzej Goscinski Emeritus Professor
2 Jemal Abawajy Professor
3 Maia Angelova Professor
4 Gleb Beliakov Professor

Answer 2

There are four ways to concat or merge dataframes, you may refer to this post
these are the most common implementations
import pandas as pd
df1 = pd.DataFrame({0:['ABCD'], 1:['WXYX']})
df2 = pd.DataFrame({0:['DEFJ'], 1:['HJKL']})
df3 = pd.DataFrame({0:['ZXCT'], 1:['WYOM']})
...
df = pd.concat([df1, df2, df3], axis=0)
print(df.head())
or if you have a list of dataframes with the same headers you can try
dfs = [df1, df2, df3 ..]
df = pd.concat(dfs, axis=0)
and the most simple way is to just use df.append
df = df.append(anotherdf)