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About Us.Q:

Memory management with pandas dataframes in python

I have a list which consists of data of different length. But there is a rule that elements should not have length more than 2500.
So I am using following code to process data.
def func(object_list):
df_temp = pd.DataFrame(object_list)
df_temp.rename(columns={“0_” : “year”, “1_” : “city”})
df_temp.rename(columns={“0_0” : “fed”})
df_temp.drop(columns=[‘0_0’])
df_temp.drop(columns=[‘1_0’])
df_temp.drop(columns=[‘2_0’])
return df_temp

My question is how to use the memory efficiently in such a case. Because I have a lot of data.

A:

You can read the data from a dictionary directly. You have to create the dictionary with keys appropriate to the structure of your data.
Here is an example. For your data there are two columns – id and title. For your dataframe you should define the index as id, and use pd.Series.from_dict to convert the key/value structure in the dictionary to data.
import pandas as pd
import numpy as np

d = {‘id’: [1, 2, 3], ‘title’: [‘hi’, ‘how are you’, ‘bye’]}
df = pd.DataFrame(d)

You can retrieve the dataframe using keys with python’s dictionary behaviour
print(df.loc[‘hi’])

id title
0 1 hi

Using this method all the columns will be named, and you can select them using a column name.
print(df[‘title’])

id title
0 1 hi

If you want to rename all the columns and rename them properly, then you can use the following command
df.columns = [‘id’, ‘title’]

You can then select columns using the column name.
print

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