I would like to create a dataframe in a loop and after use these dataframe in a loop. I tried eval() function but it didn't work.
For example :
for i in range(5):
df_i = df[(df.age == i)]
There I would like to create df_0,df_1 etc. And then concatenate these new dataframe after some calculations :
final_df = pd.concat(df_0,df_1)
for i in range(2:5):
final_df = pd.concat(final_df, df_i)
You can create a dict of DataFrames x and have is as dict keys:
np.random.seed(42)
df = pd.DataFrame({'age': np.random.randint(0, 5, 20)})
x = {}
for i in range(5):
x[i] = df[df['age']==i]
final = pd.concat(x.values())
Then you can refer to individual DataFrames as:
x[1]
Output:
age
5 1
13 1
15 1
And concatenate all of them with:
pd.concat(x.values())
Output:
age
18 0
5 1
13 1
15 1
2 2
6 2
...
exec(f"df_{i} = df[df['age']==i]"), but it's normally not recommended. See for example stackoverflow.com/questions/5036700/…The way is weird and not recommended, but it can be done.
Answer
for i in range(5):
exec("df_{i} = df[df['age']=={i}]")
def UDF(dfi):
# do something in user-defined function
for i in range(5):
exec("df_{i} = UDF(df_{i})")
final_df = pd.concat(df_0,df_1)
for i in range(2:5):
final_df = pd.concat(final_df, df_i)
Better Way 1
Using a list or a dict to store the dataframe should be a better way since you can access each dataframe by an index or a key.
Since another answer shows the way using dict (@perl), I will show you the way using list.
def UDF(dfi):
# do something in user-defined function
dfs = [df[df['age']==i] for i in range(i)]
final_df = pd.concat(map(UDF, dfs))
Better Way 2
Since you are using pandas.DataFrame, groupby function is a 'pandas' way to do what you want. (maybe, I guess, cause I don't know what you want to do. LOL)
def UDF(dfi):
# do something in user-defined function
final_df = df.groupby('age').apply(UDF)
Reference: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html
