Id like to ask for help in fixing the missing values in pandas dataframe (python)
here is the dataset
In this dataset I found a missing value in ['Item_Weight'] column.
I don't want to drop the missing values because I found out by sorting them. the missing value is "miss type" by someone who encoded it.
here is the sorted dataset
Now I created a lookup dataset so I can merge them to fill na missing values.
How can I merge them or join them only to fill the missing values (Nan) using the lookup table I made? Or is there any other way without using a lookup table?
Looking at this you will probably want to use something along the lines of map instead of join/merge this is an example of how to use map with your data.
import pandas as pd
import numpy as np
df = pd.DataFrame({
'Column1' : ['A', 'B', 'C'],
'Column2' : [1, np.nan, 3]
})
df
df_map = pd.DataFrame({
'Column1' : ['A', 'B', 'C'],
'Column2' : [1, 2, 3]
})
df_map
#Looks to find where the column you specify is null, then using your map df will map the value from column1 to column2
df['Column2'] = np.where(df['Column2'].isna(), df['Column1'].map(df_map.set_index('Column1')['Column2']), df['Column2'])
I had to create my own dataframes since you used screenshots. In the future, the use of screenshots is not considered best to help developers with assistance.
This will probably work:
df = df.sort_values(['Item_Identifier', 'Item_Weight']).ffill()
But I can't test it since you didn't give us anything to work with.
Related
So want to determine what values are in a Pandas Dataframe:
import pandas as pd
d = {'col1': [1,2,3,4,5,6,7], 'col2': [3, 4, 3, 5, 7,22,3]}
df = pd.DataFrame(data=d)
col2 hast the unique values 3,4,5,6,22 (domain). Each value that exists shall be determined. But only once.
Is there anyway to fastly extract what the domain is in a Pandas Dataframe Column?
Use df.max() and df.min() to find the range.
print(df["col2"].unique())
by Andrej Kesely is the solution. Perfect!
In the screenshot, 'Ctrl' column contains a key value. I have two duplicate rows for OTC-07 which I need to consolidate. I would like to concat the rest of column values for OTC-07. i.e, OTC-07 should have Type A,B and Assertion a,b,c,d after consolidation.. Can anyone help me on this? :o
First define a dataframe of given structure:
import pandas as pd
import numpy as np
df = pd.DataFrame({
'Ctrl': ['OTC-05', 'OTC-06', 'OTC-07', 'OTC-07', 'OTC-08'],
'Type': ['A', 'A', 'A', 'B', np.NaN],
'Assertion': ['a,b,c', 'c,b', 'a,c', 'b,c,d', 'a,b,c']
})
df
Output:
Then replace NaN values with empty strings:
df = df.replace(np.NaN, '', regex=True)
Then group by column 'Ctrl' and aggregate columns 'Type' and 'Assertion'. Please not that assertion aggregation is a bit tricky as you need not a simple concatenation, but sorted list of unique letters:
df.groupby(['Ctrl']).agg({
'Type': ','.join,
'Assertion': lambda x: ','.join(list(sorted(set(','.join(x).split(',')))))
})
Output:
I have a dataframe with a very large number of columns of different types. I want to encode the categorical variables in my dataframe using get_dummies(). The question is: is there a way to get the column headers of the encoded categorical columns created by get_dummies()?
The hard way to do this would be to extract a list of all categorical variables in the dataframe, then append the different text labels associated to each categorical variable to the corresponding column headers. I wonder if there is an easier way to achieve the same end.
I think the way that should work with all the different uses of get_dummies would be:
#example data
import pandas as pd
df = pd.DataFrame({'P': ['p', 'q', 'p'], 'Q': ['q', 'p', 'r'],
'R': [2, 3, 4]})
dummies = pd.get_dummies(df)
#get column names that were not in the original dataframe
new_cols = dummies.columns[~dummies.columns.isin(df.columns)]
new_cols gives:
Index(['P_p', 'P_q', 'Q_p', 'Q_q', 'Q_r'], dtype='object')
I think the first column is the only column preserved when using get_dummies, so you could also just take the column names after the first column:
dummies.columns[1:]
which on this test data gives the same result:
Index(['P_p', 'P_q', 'Q_p', 'Q_q', 'Q_r'], dtype='object')
I am wondering if anyone can help. I have a number of dataframes stored in a dictionary. I simply want to access each of these dataframes and count the values in a column in the column I have 10 letters. In the first dataframe there are 5bs and 5 as. For example the output from the count I would expect to be is a = 5 and b =5. However for each dataframe this count would be different hence I would like to store the output of these counts either into another dictionary or a separate variable.
The dictionary is called Dict and the column name in all the dataframes is called letters. I have tried to do this by accessing the keys in the dictionary but can not get it to work. A section of what I have tried is shown below.
import pandas as pd
for key in Dict:
Count=pd.value_counts(key['letters'])
Count here would ideally change with each new count output to store into a new variable
A simplified example (the actual dataframe sizes are max 5000,63) of the one of the 14 dataframes in the dictionary would be
`d = {'col1': [1, 2,3,4,5,6,7,8,9,10], 'letters': ['a','a','a','b','b','a','b','a','b','b']}
df = pd.DataFrame(data=d)`
The other dataframes are names df2,df3,df4 etc
I hope that makes sense. Any help would be much appreciated.
Thanks
If you want to access both key and values when iterating over a dictionary, you should use the items function.
You could use another dictionary to store the results:
letter_counts = {}
for key, value in Dict.items():
letter_counts[key] = value["letters"].value_counts()
You could also use dictionary comprehension to do this in 1 line:
letter_counts = {key: value["letters"].value_counts() for key, value in Dict.items()}
The easiest thing is probably dictionary comprehension:
d = {'col1': [1, 2,3,4,5,6,7,8,9,10], 'letters': ['a','a','a','b','b','a','b','a','b','b']}
d2 = {'col1': [1, 2,3,4,5,6,7,8,9,10,11], 'letters': ['a','a','a','b','b','a','b','a','b','b','a']}
df = pd.DataFrame(data=d)
df2 = pd.DataFrame(d2)
df_dict = {'d': df, 'd2': df2}
new_dict = {k: v['letters'].count() for k,v in df_dict.items()}
# out
{'d': 10, 'd2': 11}
I need to pick out a few rows in my sframe by index. Is there an equivalent graphlab command to pandas df.irow()?
There is no direct equivalent in graphlab to DataFrame.iloc (previously irow). One way to achieve the same thing is to add a column of row numbers and use the filter_by method. Suppose I want to get only the 1st and 3rd rows:
import graphlab
sf = graphlab.SFrame({'x': ['a', 'b', 'a', 'c']})
sf = sf.add_row_number('row_id')
new_sf = sf.filter_by(values=[0, 2], column_name='row_id')