CODEWITHSUNDEEP
pythonpandasdataframe
Analyst
Analyst

Reputation: 31

Creating Multiple DataFrames from single DataFrame based on different values of single column

I have 3 days of time series data with multiple columns in it. I have one single DataFrame which includes all 3 days data. I want 3 different DataFrames based on Column name "Dates" i.e df["Dates"]

For Example:

Available Dataframe is: df

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Expected Output: Based on Three different Dates

First DataFrame: df_23

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Second DataFrame: df_24

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Third DataFrame: df_25

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I want to use these all three DataFrames separately for analysis.

I tried below code but I am not able to use those three dataframes (Rather I don't know how to use.) Can anybody help me to work my code better. Thank you.

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Above code is just printing the DataFrame in three DataFrames that too not as expected as per code!

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Upvotes: 0

Views: 2485

Answers (1)

Umar.H
Umar.H

Reputation: 23099

Unsure if your saving your variable into a csv or keep it in memory for further use,

you could pass each unique value into a dict and access by it's value : 

    print(df)
     Cal  Dates
0    85     23
1    75     23
2    74     23
3    97     23
4    54     24
5    10     24
6    77     24
7    95     24
8    58     25
9    53     25
10   44     25
11   94     25



d = {}

for frame, data in df.groupby('Dates'):
    d[f'df{frame}'] = data


print(d['df23'])
    Cal  Dates
0   85     23
1   75     23
2   74     23
3   97     23

edit updated request : 

for k,v in d.items():
    i = (v['Cal'].loc[v['Cal'] > 70].count())
    print(f"{v['Dates'].unique()[0]} --> {i} times")
23 --> 4 times
24 --> 2 times
25 --> 1 times

Upvotes: 1

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