I wish to read pandas DataFrame element by element, but it seems below code doesn't work!
import pandas as pd
df = pd.DataFrame()
df1 = pd.DataFrame({"A": [2,3,4]})
df2 = pd.DataFrame({"B": [5,6,7]})
df = pd.concat([df1, df2], axis=1)
for row in df.iterrows():
print row['A'], row['B']
What's the right way to access it element by element?
Use at and iat detailed here
Since indexing with [] must handle a lot of cases (single-label access, slicing, boolean indexing, etc.), it has a bit of overhead in order to figure out what you’re asking for. If you only want to access a scalar value, the fastest way is to use the at and iat methods, which are implemented on all of the data structures.
Similarly to loc, at provides label based scalar lookups, while, iat provides integer based lookups analogously to iloc
How about this one:
import pandas as pd
df = pd.DataFrame()
df1 = pd.DataFrame({"A": [2,3,4]})
df2 = pd.DataFrame({"B": [5,6,7]})
df = pd.concat([df1,df2], axis=1)
for row in df.index:
print df['A'][row],df['B'][row]
Output:
2 5
3 6
4 7
In this case, the following works with iterrows:
>>> for _, row in df.iterrows():
print row.A, row.B
2 5
3 6
4 7
applymap()