Arwa
0
I'm trying to add more rows or records to my data frame, let's say it looks like this:
ID age
44 23
31 25
and I have a CSV file stored in another data frame without headers
33 55
22 23
29 22
now I want a new data frame that looks like this
ID age
44 23
31 25
33 55
22 23
29 22
I have tried using append and concat but I didn't get the result that I wanted
Assuming df1/df2, you can use set_axis to copy the axis of the first DataFrame, then concat:
out = pd.concat([df1, df2.set_axis(df1.columns, axis=1)], ignore_index=True)
output:
ID age
0 44 23
1 31 25
2 33 55
3 22 23
4 29 22
NB. ignore_index=True is optional, this is just to avoid having duplicated indices. Without it:
ID age
0 44 23
1 31 25
0 33 55
1 22 23
2 29 22
A Dataframe has axes (indices) -- Row index (axis=0) --Column index (axes=1). Pandas provides various facilities for easily combining together Series, DataFrame.
pd.concat(objs, axis=0, join='outer', join_axes=None,ignore_index=False)
• objs − This is a sequence or mapping of Series, DataFrame, or Panel objects.
• axis − {0, 1, ...}, default 0. This is the axis to concatenate along.
• join − {‘inner’, ‘outer’}, default ‘outer’. How to handle indexes on other axis(es). Outer for union and inner for the intersection.
• ignore_index − boolean, default False. If True, do not use the index values on the concatenation axis. The resulting axis will be labeled 0, ..., n - 1.
• join_axes − This is the list of Index objects. Specific indexes to use for the other (n-1) axes instead of performing inner/outer set logic.
con = pd.concat([df1, df2]) # If column names are the same.
con = pd.concat([df1, df2], axis="columns")
If indices are the same between datasets. If they’re different, by default the extra indices (rows) will also be added, and NaN values will be filled.
Two DataFrames might hold different kinds of information about the same entity and are linked by some common feature/column. To join these DataFrames, pandas provides multiple functions like merge(), join() etc.
If you have an SQL background, then you may use the merge operation names from the JOIN syntax. You can use Full Outer Join, Inner Join, RightJoin, Left Join,Joining on Index.
import pandas as pd
# Your first df will be ‘df’
df_2 = pd.dataframe({“Id”: [33, 22, 29], “Age”: [55, 23, 22]})
df = df.append(df_2, ignore_index=True)
You can use this, and ignore_index if you want.
output = pd.concat([df1, df2.set_axis(df1['columns'], axis=1)])