I have a few dictionaries, and I’d like to put them into one large dataframe with custom columns.
What I have now is two separate dictionaries (I have more but I’ll use two as an example):
dict_team:
{ Team Alpha: linkToAlpha
Team Beta: linkToBeta
Team Charlie: linkToCharlie }
dict_project:
{ Project Delta: linkToDelta
Project Echo: linkToEcho
Project Fox: linkToFox }
I have these two dicts, and I’d like to combine them into one large dataframe (so that I can then turn them into an excel spreadsheet/table) like this:
Team | Team Link | Project | Project Link |
---|---|---|---|
Team Alpha | linkToAlpha | Project Delta | linkToDelta |
Team Beta | linkToBeta | Project Echo | linkToEcho |
Team Charlie | linkToCharlie | Project Fox | linkToFox |
I also want the option to not have any table names, and just have a table like the one above (but with no custom headers)
From what I understand, I need to convert all of my dictionaries (they’re already created by a function, and I can reference them by the dict names dict_team and dict_project) into a list, and from there turn that list into one big dataframe.
All of the information I found online used dictionaries that had the keys as the column names, but that’s not what I want.
If anyone can help that would be amazing. Thank you!
>Solution :
You may need to convert the two dictionaries to DataFrame first, and then merge them. Like this:
import pandas as pd
dict_team = {
"Team Alpha": "linkToAlpha",
"Team Beta": "linkToBeta",
"Team Charlie": "linkToCharlie",
}
dict_project = {
"Project Delta": "linkToDelta",
"Project Echo": "linkToEcho",
"Project Fox": "linkToFox",
}
df_team = pd.DataFrame(dict_team.items(), columns=["Team", "Team Link"])
df_project = pd.DataFrame(dict_project.items(), columns=["Project", "Project Link"])
df_merged = pd.concat([df_team, df_project], axis=1)
print(df_merged)