I have a list of Pandas dataframes that I would like to combine into one Pandas dataframe. I am using Python 2.7.10 and Pandas 0.16.2
I created the list of dataframes from:
import pandas as pd dfs = [] sqlall = "select * from mytable" for chunk in pd.read_sql_query(sqlall , cnxn, chunksize=10000): dfs.append(chunk) This returns a list of dataframes
type(dfs[0]) Out[6]: pandas.core.frame.DataFrame type(dfs) Out[7]: list len(dfs) Out[8]: 408 Here is some sample data
# sample dataframes d1 = pd.DataFrame({'one' : [1., 2., 3., 4.], 'two' : [4., 3., 2., 1.]}) d2 = pd.DataFrame({'one' : [5., 6., 7., 8.], 'two' : [9., 10., 11., 12.]}) d3 = pd.DataFrame({'one' : [15., 16., 17., 18.], 'two' : [19., 10., 11., 12.]}) # list of dataframes mydfs = [d1, d2, d3] I would like to combine d1, d2, and d3 into one pandas dataframe. Alternatively, a method of reading a large-ish table directly into a dataframe when using the chunksize option would be very helpful.
6 Answers
Given that all the dataframes have the same columns, you can simply concat them:
import pandas as pd df = pd.concat(list_of_dataframes) Just to add few more details:
Example:
list1 = [df1, df2, df3] import pandas as pd Row-wise concatenation & ignoring indexes
pd.concat(list1, axis=0, ignore_index=True)Note: If column names are not same then NaN would be inserted at different column values
Column-wise concatenation & want to keep column names
pd.concat(list1, axis=1, ignore_index=False)If ignore_index=True, column names would be filled with numbers starting from 0 to (n-1), where n is the count of unique column names
If the dataframes DO NOT all have the same columns try the following:
df = pd.DataFrame.from_dict(map(dict,df_list)) You also can do it with functional programming:
from functools import reduce reduce(lambda df1, df2: df1.merge(df2, "outer"), mydfs) concat also works nicely with a list comprehension pulled using the "loc" command against an existing dataframe
df = pd.read_csv('./data.csv') # ie; Dataframe pulled from csv file with a "userID" column review_ids = ['1','2','3'] # ie; ID values to grab from DataFrame # Gets rows in df where IDs match in the userID column and combines them dfa = pd.concat([df.loc[df['userID'] == x] for x in review_ids]) panders concat works also as well in addition with functools
from functors import reduce as reduce import pandas as pd; deaf = pd.read_csv("") for q in range(0, Len(deaf)): new = map(lambda x: reduce(pd.concat(x)) 
