I want to write a piece of code to create multiple arrays of DataFrames with their names in the format of <name>_mmyy, where the mm is a month and yy is a year.
Here is an example:
df_0115, df_0215, df_0315, ... , df_1215
stat_0115, stat_0215, stat_0315, ... , stat_1215
I suggest that you create a dictionary to hold the DataFrames. That way you will be able to index them with a month-day key:
import datetime as dt
import numpy as np
import pandas as pd
dates_list = [dt.datetime(2015,11,i+1) for i in range(3)]
month_day_list = [d.strftime("%m%d") for d in dates_list]
dataframe_collection = {}
for month_day in month_day_list:
new_data = np.random.rand(3,3)
dataframe_collection[month_day] = pd.DataFrame(new_data, columns=["one", "two", "three"])
for key in dataframe_collection.keys():
print("\n" +"="*40)
print(key)
print("-"*40)
print(dataframe_collection[key])
The code above prints out the following result:
========================================
1102
----------------------------------------
one two three
0 0.896120 0.742575 0.394026
1 0.414110 0.511570 0.268268
2 0.132031 0.142552 0.074510
========================================
1103
----------------------------------------
one two three
0 0.558303 0.259172 0.373240
1 0.726139 0.283530 0.378284
2 0.776430 0.243089 0.283144
========================================
1101
----------------------------------------
one two three
0 0.849145 0.198028 0.067342
1 0.620820 0.115759 0.809420
2 0.997878 0.884883 0.104158
new_dataframe =A and dataframe_collection[month_day] = new_dataframe like this? I just did dataframe_collection[month_day] = A.
new_dataframe intermediate variable. I updated the answer to reflect that. As far as the random order in which the result is printed, this has to do with python's implementation of the dictionary. The dictionary key-value pairs are stored in a data structure called a hash table. This data structure is designed for very fast lookups and, as part of the algorithm to achieve this, the way the keys are stored in it can be random.collections module and use an OrderedDict rather than a plain dict to collect your dataframes: dataframe_collection = collections.OrderedDict()df will have all the CSV files you need.
df[0] to access first one
df=[]
files = glob.glob("*.csv")
for a in files:
df.append( pd.read_csv(a))
df['0115'], df['0215'], stat['0115'], stat['0215'], etc,