dataframe from arrays python
import pandas as pd
df=pd.DataFrame({'col1':vect1,'col2':vect2})dataframe from arrays python
import pandas as pd
df=pd.DataFrame({'col1':vect1,'col2':vect2})pandas array of dataframes
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 = {} # dictionary to store dataframes - generally better than an array
for month_day in month_day_list: # for each key
# Create/ assign your new data:
new_data = np.random.rand(3,3)
# Store the new data in the dictionary:
dataframe_collection[month_day] = pd.DataFrame(new_data, columns=["one", "two", "three"])
# Neat printing code:
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 0.104158list dataframe to numpy array
df.values
array([[nan, 0.2, nan],
[nan, nan, 0.5],
[nan, 0.2, 0.5],
[0.1, 0.2, nan],
[0.1, 0.2, 0.5],
[0.1, nan, 0.5],
[0.1, nan, nan]])Copyright © 2021 Codeinu
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