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How to generate multiple dataframes from a dictionary?

I have a dictionary like the following
dict = {“df_text":df1, “df_logo":df2, “df_person":df3}
Each of the values in the dictionary is a dataframe.
Yet my actual dictionary is larger, so I want to make a loop that generate multiple dataframes from all of the components of this dict. In a way that “key” be the name of the dataframe and the corresponding value the dataframe’s value.
ex.
df_text=pd.DataFrame(df1)
How can I do this?

You can add the contents of your dict as variables to vars:
for k, v in dict.items():
vars()[k] = v
After that you can access them simply as df_text, df_logo etc.
(as you wrote in your question, the values of your dict are already dataframe, so I assume you don't want to wrap them once more into a dataframe)

Converting a dataframe to a dictionary with tuples as values

I am trying to convert a Pandas DataFrame to a dictionary. I would like to have pid be the key and the remaining two columns be values within the tuples.
I have tried aggregated_events.set_index('pid').to_dict('list') and aggregated_events.set_index('pid').to_dict() but know I am missing something. Any help would be greatly appreciated!
Original Dataframe

You can first transpose your dataframe to get first column as new column names, something like this:
df = df.set_index('pid').T
Then you can use to_dict to convert a dataframe to a dictionary.

How to access values from list of dictionaries in a dataframe?

I have a dataframe that has a column with a list of dictionaries and for each dictionary I want to be able to extract the values and put them in another column as list. Please see the picture below for example which shows only 1 row of the dataframe. so for each title shown on the picture I want to extract the values and put them in a list for all the rows in a dataframe

Use ast.literal_eval to convert the string as a list of dict then extract the `title keys from each records:
import ast
df['activities'].apply(lambda x: [d['title'] for d in ast.literal_eval(x)])

Create a dictionary from DataFrame?

I want to create a dictionary from a dataframe in python.
In this dataframe, frame one column contains all the keys and another column contains multiple values of that key.
DATAKEY DATAKEYVALUE
name mayank,deepak,naveen,rajni
empid 1,2,3,4
city delhi,mumbai,pune,noida
I tried this code to first convert it into simple data frame but all the values are not separating row-wise:
columnnames=finaldata['DATAKEY']
collist=list(columnnames)
dfObj = pd.DataFrame(columns=collist)
collen=len(finaldata['DATAKEY'])
for i in range(collen):
colname=collist[i]
keyvalue=finaldata.DATAKEYVALUE[i]
valuelist2=keyvalue.split(",")
dfObj = dfObj.append({colname: valuelist2}, ignore_index=True)

You should modify you title question, it is misleading because pandas dataframes are "kind of" dictionaries in themselves, that is why the first comment you got was relating to the .to_dict() pandas' built-in method.
What you want to do is actually iterate over your pandas dataframe row-wise and for each row generate a dictionary key from the first column, and a dictionary list from the second column.
For that you will have to use:
an empty dictionary: dict()
the method for iterating over dataframe rows: dataframe.iterrows()
a method to split a single string of values separated by a separator as the split() method you suggested: str.split().
With all these tools all you have to do is:
output = dict()
for index, row in finaldata.iterrows():
output[row['DATAKEY']] = row['DATAKEYVALUE'].split(',')
Note that this generates a dictionary whose values are lists of strings. And it will not work if the contents of the 'DATAKEYVALUE' column are not singles strings.
Also note that this may not be the most efficient solution if you have a very large dataframe.

Element-wise dictionary creation from two pandas series (columns of csv as DataFrame)

I'm looking to do a row by row dictionary mapping of two columns of a headered multi-column csv read in to a pandas DataFrame via read_csv(). In other words, for each row, make the value in column X the key, and the corresponding value in column Y the value.
With many operations in pandas being element-wise, I surmised that I could possibly achieve this with the following one-liner:
{df['X'] : df['Y']}
Alas, no such luck.
Does pandas expose its own way of doing this? I'll also be looking to something similar with a .xls and a .xlsx.
Python: 2.7.11
Pandas: 0.18

How about:
dict(zip(df['X'].values, df['Y'].values))

If all of the values in X are not unique, then you will be overwriting your keys.
Assuming that they are unique, then you can use a dictionary comprehension with zip.
{key: val for key, val in zip(df.X, df.Y)}