I have a DataFrame object similar to this one:
onset length 1 2.215 1.3 2 23.107 1.3 3 41.815 1.3 4 61.606 1.3 ... What I would like to do is insert a row at a position specified by some index value and update the following indices accordingly. E.g.:
onset length 1 2.215 1.3 2 23.107 1.3 3 30.000 1.3 # new row 4 41.815 1.3 5 61.606 1.3 ... What would be the best way to do this?
You could slice and use concat to get what you want.
from pandas import DataFrame, concat line = DataFrame({"onset": 30.0, "length": 1.3}, index=[3]) df2 = concat([df.iloc[:2], line, df.iloc[2:]]).reset_index(drop=True) This will produce the dataframe in your example output. As far as I'm aware, concat is the best method to achieve an insert type operation in pandas, but admittedly I'm by no means a pandas expert.
import pandas as pd, might will need to add pd. before DataFrame and concat, as pd.DataFrame, else one will get a NameError: name 'DataFrame' is not defined.
.reset_index(drop=True). This also prevents losing the oldest duplicate row.I think it's even easier without concat or append:
df.loc[2.5] = 30.0, 1.3 df = df.sort_index().reset_index(drop=True) (Supposing that the index is as provided, starting from 1)
I find it more readable to sort rather than slice and concatenate.
line = DataFrame({"onset": 30.0, "length": 1.3}, index=[2.5]) df = df.append(line, ignore_index=False) df = df.sort_index().reset_index(drop=True) 
frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.If you want to keep the original indexes this might work beter:
df = pd.DataFrame(dict(x=[0, 1, 2, 3, 4])) df_update = pd.DataFrame(dict(x=[10, 11, 12]), index=[3, 4, 5]) # concat df_update first df = pd.concat([df_update, df], axis=0) # drop duplicates, updates will be prioritized df = df.iloc[df.index.drop_duplicates()] # sort to regain order df.sort_index(inplace=True) 
df1 = pd.DataFrame(np.insert(df1.values, index+1, values=[" "] * len(df1.columns), axis=0),columns = df1.columns)