How do i remove nan values from dataframe in Python? I already tried with dropna(), but that did not work for me. Also is NaN diffferent from nan. I am using Pandas.
While printing the data frame it does not print as NaN but instead as nan.
1 2.11358 0.649067060588935
2 nan 0.6094130485307419
3 2.10066 0.3653980276694516
4 2.10545 nan
You can change nan values with NaN using replace() and then use dropna().
import numpy as np
df = df.replace('nan', np.nan)
df = df.dropna()
Update:
Original dataframe:
1 2.11358 0.649067060588935
2 nan 0.6094130485307419
3 2.10066 0.3653980276694516
4 2.10545 nan
Applied df.replace('nan', np.nan):
1 2.11358 0.649067060588935
2 NaN 0.6094130485307419
3 2.10066 0.3653980276694516
4 2.10545 NaN
Applied df.dropna():
1 2.11358 0.649067060588935
3 2.10066 0.3653980276694516
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I have a multi indexed data frame as the attached image. Now I need to create a new column on a different data frame where each column name will be the unique room numbers.
For example, one expected output from the code will be as following:
N.B. I want to avoid for loops to save memory space and time. What would be the optimal way to get desired output ?
I have tried using for loops and could get desired output but I am not sure if it
s a good idea for a large dataset. Here is the code snippet :
import numpy as np
import pandas as pd
d = np.array(['624: COUPLE , 507: DELUXE+ ,301: HONEYMOON','624:FAMILY ,
507: FAMILY+','621:FAMILY , 517: FAMILY+','696:FAMILY , 585:
FAMILY+,624:FAMILY , 507: DELUXE'])
df = pd.Series(d)
df= df.str.extractall(r'(?P<room>[0-9]+):\s*(?P<grd>[^\s,]+)')
gh = df[df['room'] == '507'].index
rf = pd.DataFrame(index=range(0,4),columns=['room#507','room#624'],
dtype='float')
for i in range(0,rf.shape[0]):
for j in range(0,gh.shape[0]):
if (i == gh[j][0]):
rf['room#507'][i] = df.grd[gh[j][0]][gh[j][1]]
Use DataFrame.reset_index with DataFrame.pivot:
df= df.str.extractall(r'(?P<room>[0-9]+):\s*(?P<grd>[^\s,]+)')
df = df.reset_index(level=1, drop=True).reset_index()
df = df.pivot('index','room','grd').add_prefix('room_')
print (df)
room room_301 room_507 room_517 room_585 room_621 room_624 room_696
index
0 HONEYMOON DELUXE+ NaN NaN NaN COUPLE NaN
1 NaN FAMILY+ NaN NaN NaN FAMILY NaN
2 NaN NaN FAMILY+ NaN FAMILY NaN NaN
3 NaN DELUXE NaN FAMILY+ NaN FAMILY FAMILY
Or DataFrame.set_index with Series.unstack:
df= df.str.extractall(r'(?P<room>[0-9]+):\s*(?P<grd>[^\s,]+)')
df = (df.reset_index(level=1, drop=True)
.set_index('room', append=True)['grd']
.unstack()
.add_prefix('room_'))
print (df)
room room_301 room_507 room_517 room_585 room_621 room_624 room_696
0 HONEYMOON DELUXE+ NaN NaN NaN COUPLE NaN
1 NaN FAMILY+ NaN NaN NaN FAMILY NaN
2 NaN NaN FAMILY+ NaN FAMILY NaN NaN
3 NaN DELUXE NaN FAMILY+ NaN FAMILY FAMILY
I am trying to add a row of data to a pandas DataFrame, but it keeps adding a separate row for each piece of data. I feel I am missing something very simple and obvious, but what it is I do not know.
import pandas
colNames = ["ID", "Name", "Gender", "Height", "Weight"]
df1 = pandas.DataFrame(columns = colNames)
df1.set_index("ID", inplace=True, drop=False)
i = df1.shape[0]
person = [{"ID":i},{"Name":"Jack"},{"Gender":"Male"},{"Height":177},{"Weight":75}]
df1 = df1.append(pandas.DataFrame(person, columns=colNames))
print(df1)
Output:
ID Name Gender Height Weight
0 0.0 NaN NaN NaN NaN
1 NaN Jack NaN NaN NaN
2 NaN NaN Male NaN NaN
3 NaN NaN NaN 177.0 NaN
4 NaN NaN NaN NaN 75.0
You are using too many squiggly brackets. All of your data should be inside one pair of squiggly brackets. This creates a single python dictionary. Change that line to:
person = [{"ID":i,"Name":"Jack","Gender":"Male","Height":177,"Weight":75}]
Full disclosure. I'm fairly new to Python and discovered PANDAS today.
I created a Dataframe from two csv files, one which is the results of a robot scanning barcode IDs and one which is a list of instructions for the robot to execute.
import pandas as pd
#import csv file and read the column containing plate IDs scanned by Robot
scancsvdata = pd.read_csv("G:\scan.csv", header=None, sep=';', skiprows=(1),usecols=[6])
#Rename Column to Plates Scanned
scancsvdata.columns = ["IDs Scanned"]
#Remove any Duplicate Plate IDs
scancsvdataunique = scancsvdata.drop_duplicates()
#import the Worklist to be executed CSV file and read the Source Column to find required Plates
worklistdataSrceID = pd.read_csv("G:\TestWorklist.CSV", usecols=["SrceID"])
#Rename SrceID Column to Plates Required
worklistdataSrceID.rename(columns={'SrceID':'IDs required'}, inplace=True)
#remove duplicates from Plates Required
worklistdataSrceIDunique = worklistdataSrceID.drop_duplicates()
#import the Worklist to be executed CSV file and read the Destination Column to find required Plates
worklistdataDestID = pd.read_csv("G:\TestWorklist.CSV", usecols=["DestID"])
#Rename DestID Column to Plates Required
worklistdataDestID.rename(columns={'DestID':'IDs required'}, inplace=True)
#remove duplicates from Plates Required
worklistdataDestIDunique = worklistdataDestID.drop_duplicates()
#Combine into one Dataframe
AllData = pd.concat ([scancsvdataunique, worklistdataSrceIDunique, worklistdataDestIDunique], sort=True)
print (AllData)
The resulting Dataframe lists IDs scanned in Column 1 and IDs required in Column 2.
IDs Scanned IDs required
0 1024800.0 NaN
1 1024838.0 NaN
2 1024839.0 NaN
3 1024841.0 NaN
4 1024844.0 NaN
5 1024798.0 NaN
6 1024858.0 NaN
7 1024812.0 NaN
8 1024797.0 NaN
9 1024843.0 NaN
10 1024840.0 NaN
11 1024842.0 NaN
12 1024755.0 NaN
13 1024809.0 NaN
14 1024810.0 NaN
15 8656.0 NaN
16 8657.0 NaN
17 8658.0 NaN
0 NaN 1024800.0
33 NaN 1024843.0
0 NaN 8656.0
7 NaN 8657.0
15 NaN 8658.0
How would I go about ensuring that all the IDs in the 'IDs Required' Column, appear in the 'IDs Scanned Column'?
Ideally the results of the comparison above would be a generic message like 'All IDs found'.
If different csv files were used and the Dataframe was as follows
IDs Scanned IDs required
0 1024800.0 NaN
1 1024838.0 NaN
2 1024839.0 NaN
3 1024841.0 NaN
4 1024844.0 NaN
5 1024798.0 NaN
6 1024858.0 NaN
7 1024812.0 NaN
8 1024797.0 NaN
9 1024843.0 NaN
10 1024840.0 NaN
11 1024842.0 NaN
12 1024755.0 NaN
13 1024809.0 NaN
14 1024810.0 NaN
15 8656.0 NaN
16 8657.0 NaN
17 8658.0 NaN
0 NaN 2024800.0
33 NaN 2024843.0
0 NaN 8656.0
7 NaN 8657.0
15 NaN 8658.0
Then the result of the comparison would be the list of the missing IDs, 2024800 and 2024843.
To check True/False if all the items required are in the column;
all([item in df["IDs Scanned"] for item in df["IDs required"].unique()])
To get a list of the unique missing items:
sorted(set(df["IDs required"]) - set(df["IDs Scanned"]))
Or using pandas syntax to return a DataFrame filtered to rows where IDs required are not found in IDs Scanned:
df.loc[~df["IDs required"].isin(df["IDs Scanned"])]
missing_ids = df.loc[~df['IDs required'].isin(df['IDs Scanned']), 'IDs required']
I have two dataframe of shape (6,3) & (2,3). Now I want to reindex second dataframe like first dataframe and also fill na values with either ffill method or bfill method. my code is as follows:
df1 = pd.DataFrame(np.random.randn(6,3),columns = ['Col1','Col2','Col3'])
df2 = pd.DataFrame(np.random.randn(2,3),columns = ['Col1','Col2','Col3'])
df2 = df2.reindex_like(df1,method='ffill')
But this code is not working well as I am getting following result:
Col1 Col2 Col3
0 0.578282 -0.199872 0.468505
1 1.086811 -0.707933 -0.924984
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
Any suggestion would be great
I'm appending data from a list to pandas df. I keep getting NaN in my entries.
Based on what I've read I think I might have to mention the data type for each column in my code.
dumps = [];features_df = pd.DataFrame()
for i in range (int(len(ids)/50)):
dumps = sp.audio_features(ids[i*50:50*(i+1)])
for i in range (len(dumps)):
print(list(dumps[0].values()))
features_df = features_df.append(list(dumps[0].values()), ignore_index = True)
Expected results, something like-
[0.833, 0.539, 11, -7.399, 0, 0.178, 0.163, 2.1e-06, 0.101, 0.385, 99.947, 'audio_features', '6MWtB6iiXyIwun0YzU6DFP', 'spotify:track:6MWtB6iiXyIwun0YzU6DFP', 'https://api.spotify.com/v1/tracks/6MWtB6iiXyIwun0YzU6DFP', 'https://api.spotify.com/v1/audio-analysis/6MWtB6iiXyIwun0YzU6DFP', 149520, 4]
for one row.
Actual-
danceability energy ... duration_ms time_signature
0 NaN NaN ... NaN NaN
1 NaN NaN ... NaN NaN
2 NaN NaN ... NaN NaN
3 NaN NaN ... NaN NaN
4 NaN NaN ... NaN NaN
5 NaN NaN ... NaN NaN
For all rows
append() strategy in a tight loop isn't a great way to do this. Rather, you can construct an empty DataFrame and then use loc to specify an insertion point. The DataFrame index should be used.
For example:
import pandas as pd
df = pd.DataFrame(data=[], columns=['n'])
for i in range(100):
df.loc[i] = i
print(df)
time python3 append_df.py
n
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
real 0m13.178s
user 0m12.287s
sys 0m0.617s
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.append.html
Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once.