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}]
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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 pivot this data in such a way that I get columns like eg: AK_positive AK_probableCases, AK_negative, AL_positive.. and so on.
You can get the data here, df = pd.read_csv('https://covidtracking.com/api/states/daily.csv')
Just flatten the original MultiIndex column into tuples using .to_flat_index(), and rearrange tuple elements into a new column name.
df_pivoted.columns = [f"{i[1]}_{i[0]}" for i in df_pivoted.columns.to_flat_index()]
Result:
# start from April
df_pivoted[df_pivoted.index >= 20200401].head(5)
AK_positive AL_positive AR_positive ... WI_grade WV_grade WY_grade
date ...
20200401 133.0 1077.0 584.0 ... NaN NaN NaN
20200402 143.0 1233.0 643.0 ... NaN NaN NaN
20200403 157.0 1432.0 704.0 ... NaN NaN NaN
20200404 171.0 1580.0 743.0 ... NaN NaN NaN
20200405 185.0 1796.0 830.0 ... NaN NaN NaN
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
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.