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What's the most efficient way to export multiple pandas dataframes to csv files?

I have multiple pandas dataframes:
df1
df2
df3
And I want to export them all to csv files.
df1.to_csv('df1.csv', index = False)
df2.to_csv('df2.csv', index = False)
df3.to_csv('df3.csv', index = False)
What's the most efficient way to do this?
def printToCSV(number):
num = str(num)
csvTemp = "df99.csv"
csv = csvTemp.replace('99',num)
dfTemp = "df99"
dfString = dfTemp.replace('99',num)
#i know i cant use the .to_csv function on a string
#but i dont know how iterate through different dataframes
dfString.to_csv(csv, index = False)
for i in range(1,4):
printToCSV(i)
How can I can call different dataframes to export?

You can add them all to a list with:
list_of_dataframes = [df1, df2, df3]
Then you can iterate through the list using enumerate to keep a going count of which dataframe you are writing, using f-strings for the filenames:
for count, dataframe in enumerate(list_of_dataframes):
dataframe.to_csv(f"dataframe_{count}.csv", index=False)

When you are creating the dataframes you can store them in a suitable data structure like list and when you want to create csv you can use map and do the same.
dfs = []
for i in range(10):
dfs.append(DataFrame())
result = [dfs[i].to_csv(f'df{i}') for i in range(10)]

If you want to stick with your function approach, use:
df_dict = {"df1": df1, "df2": df2, "df3": df3}
def printToCSV(num):
df_dict[f"df{num}"].to_csv(f"df{num}.csv", index=False)
printToCSV(1) # will create "df1.csv"
However, if you want to increase efficiency, I'd propose to use a df list (as #vtasca proposed as well):
df_list = [df1, df2, df3]
for num, df in enumerate(df_list): # this will give you a number (starting from 0) and the df, in each loop
df.to_csv(f"df{num}.csv", index=False)
Or working with a dict:
df_dict = {"df1": df1, "df2": df2, "df3": df3}
for name, df in df_dict.items():
df.to_csv(f"{name}.csv", index=False)

Mapping Two dataframes Pandas

I want to map two dataframes in pandas , in DF1 I have
df1
my second dataframe looks like
df2
I want to merge the two dataframes and get something like this
merged DF
on the basis of the 1 occuring in the DF1 , it should be replaced by the value after merging
so far i have tried
mergedDF = pd.merge(df1,df2, on=companies)

Seems like you need .idxmax() method.
merged = df1.merge(df2, on='Company')
merged['values'] = merged[[x for x in merged.columns if x != 'Company']].idxmax(axis=1)

Concatenate multiple dataframe and columns names

I have a list of data-frames
liste = [df1, df2, df3, df4]
sharing same index called "date". I concatenate this as follow:
pd.concat( (dd for dd in ll ), axis=1, join='inner')
But the columns have the same name. I can override the columns name manually, but I wonder if there is a way that the columns name will take the corresponding data-frame names, in this case "df1", "df2".

You can replace them as followes:
import pandas as pd
from functools import reduce
liste = [df1, df2, df3, df4]
df_final = reduce(lambda left,right: pd.merge(left,right,on='name'), liste)
Or:
... code snippet ...
df1.merge(df2,on='col_name').merge(df3,on='col_name').merge(df4,on='col_name')
Update based on comment:
An example for automated grabbing the column names of each you may integrate below code (while I assume its a single column array) to your liking:
colnames = {}
for i in range(len(dfs)):
name = df[i].columns
colnames[i+1] = name
... merge with code above ...

you could use merge
df=liste[0]
for data_frame in liste[1:]:
df=df.merge(date_frame, left_index=True,right_index=True)
by default you'll get y_ appended to the columns so you'll end up with _y_y etc but you can control this with suffixes= so perhaps you use the position with an enumerate in the loop?

How to call a list of DataFrames in a function? [duplicate]

I have multiple dataframes:
df1, df2, df3,..., dfn
They have the same type of data but from different groups of descriptors that cannot be joined. Now I need to apply the same function to each dataframe manually.
How can I apply the same function to multiple dataframes?

pipe + comprehension
If your dataframes contain related data, as in this case, you should store them in a list (if numeric ordering is sufficient) or dict (if you need to provide custom labels to each dataframe). Then you can pipe each dataframe through a function foo via a comprehension.
List example
df_list = [df1, df2, df3]
df_list = [df.pipe(foo) for df in df_list]
Then access your dataframes via df_list[0], df_list[1], etc.
Dictionary example
df_dict = {'first': df1, 'second': df2, 'third': df3}
df_dict = {k: v.pipe(foo) for k, v in df_dict.items()}
Then access your dataframes via df_dict['first], df_dict['second'], etc.

If the data frames have the same columns you could concat them to a single data frame, but otherwise there is not really a "smart" way of doing it:
df1, df2, df3 = (df.apply(...) for df in [df1, df2, df3]) # or either .map or .applymap

How to combine (merge) an array of identical DataFrames into a single one?

How do I merge or combine an array of DataFrames in pandas?
dfs = []
for df in pd.read_csv(....chunksize=chunk_size):
df1 = df
# ....
if condition:
dfs.append(df1)
As you can see, they all have the same structure, I just need to combine them in a single DataFrame.

normally you can concatenate your array of data frame so you could have
dfs = []
for df in pd.read_csv(....chunksize=chunk_size):
df1 = df
# ....
if condition:
dfs.append(df1)
result=concat(dfs)
you can find more info on this , here.

Pandas DataFrame already has an append method to merge two DataFrames
See the documentation
import pandas
dfs = pandas.DataFrame()
for df in pd.read_csv(....chunksize=chunk_size):
df1 = df
# ....
if condition:
dfs.append(df1)