Add Header To Pandas Dataframe Code Example


Example 1: how to add column headers in pandas

#suppose team is a df that has unnamed columns (0,1...) team.columns =['Name', 'Code', 'Age', 'Weight']

Example 2: how to add headings to data in pandas

Cov = pd.read_csv("path/to/file.txt", sep='\t') Frame=pd.DataFrame(Cov.values, columns = ["Sequence", "Start", "End", "Coverage"]) Frame.to_csv("path/to/file.txt", sep='\t')

Example 3: how to create new header of a dataframe in python

pythonCopy# python 3.x import pandas as pd import numpy as np  df = pd.DataFrame(data=np.random.randint(0, 10, (6,4)))  df.columns=["a", "b", "c", "d"] print(df)

Example 4: pandas heading

pd.read_csv("directory", heading = None)

Comments

Post a Comment

Popular posts from this blog

Are Regular VACUUM ANALYZE Still Recommended Under 9.1?

Answer : VACUUM is only needed on updated or deleted rows in non-temporary tables. Obviously you're doing lots of INSERTs but it's not obvious from the description that you're also doing lots of UPDATEs or DELETEs. These operations can be tracked with the pg_stat_all_tables view, specifically the n_tup_upd and n_tup_del columns. Also, even more to the point, there is a n_dead_tup column that tells, per table, how much rows need to be vacuumed. (see Monitoring statistics in the doc for functions and views related to statistics gathering). A possible strategy in your case would be to suppress the scheduled VACUUM, keeping an eye on this view and checking on which tables the n_dead_tup is going up significantly. Then apply the aggressive VACUUM to these tables only. This will be a win if there are large tables whose rows never get deleted nor updated and the aggressive VACUUM is really necessary only on smaller tables. But keep running the ANALYZE for the optimiz
Read more

Can Feynman Diagrams Be Used To Represent Any Perturbation Theory?

Answer : Diagram machinery works also for perturbation theory in classical statistical mechanics and classical field theories. Generally, various kinds of diagrams constitute a pictorial way of talking about tensor products and their contractions while hiding the multi-linear algebra from the layman. In the simplest case, vertices (or blobs) represent vectors, matrices, tensors; vertices have ingoing and/or outgoing ports; ingoing ports denote (contravariant) ro indices; outgoing ports denote (covariant) co indices; directed arcs between blobs give a pair of equal indices summed over; different base spaces ⇔ \Leftrightarrow ⇔ different arc types; symmetric tensors ⇔ \Leftrightarrow ⇔ undirected diagrams; no labels are needed for internal lines. In many cases, the directed arcs are decorated (as thin, thick, broken,wavy, curly lines), each decoration indicating the presence of a so-called propagator, a function of its label to use as a weight in the sum, which may beco
Read more