Concatenate a list of pandas dataframes together
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.
Solution 1:
Given that all the dataframes have the same columns, you can simply concat
them:
import pandas as pd
df = pd.concat(list_of_dataframes)
Solution 2:
If the dataframes DO NOT all have the same columns try the following:
df = pd.DataFrame.from_dict(map(dict,df_list))
Solution 3:
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
Solution 4:
You also can do it with functional programming:
from functools import reduce
reduce(lambda df1, df2: df1.merge(df2, "outer"), mydfs)