I encountered an error on this code. As I execute the second line of the code, it overwrites the output of the 1st line. How can I fix it?
df.loc[df['col1'] < 200_000, 'col1'] += 40_000
df.loc[df['col1'] > 195_959, 'col1'] -= 200_000
Here is my dataframe. In column 1, I want to add 40000.0 to all values <200000.0, and subtract 200000.0 to all values >195959.0.
print(df)
col1 col2
0 193431.0 40955.41
1 193432.0 40955.63
2 193433.0 40955.89
3 193434.0 40956.31
4 193435.0 40956.43
... ...
29752 235023.0 40934.89
29753 235024.0 40935.00
29754 235025.0 40935.13
29755 235026.0 40934.85
29756 235027.0 40935.18
Here is my expected output.
print(new_df)
col1 col2
0 233431.0 40955.41
1 233432.0 40955.63
2 233433.0 40955.89
3 233434.0 40956.31
4 233435.0 40956.43
... ...
29752 35023.0 40934.89
29753 35024.0 40935.00
29754 35025.0 40935.13
29755 35026.0 40934.85
29756 35027.0 40935.18
Related
I've a dataframe which looks like this:
wave mean median mad
0 4050.32 -0.016182 -0.011940 0.008885
1 4208.98 0.023707 0.007189 0.032585
2 4508.28 3.662293 0.001414 7.193139
3 4531.62 -15.459313 -0.001523 30.408377
4 4551.65 0.009028 0.007581 0.005247
5 4554.46 0.001861 0.010692 0.027969
6 6828.60 -10.604568 -0.000590 21.084799
7 6839.84 -0.003466 -0.001870 0.010169
8 6842.04 -32.751551 -0.002514 65.118329
9 6842.69 18.293519 -0.002158 36.385884
10 6843.66 0.006386 -0.002468 0.034995
11 6855.72 0.020803 0.000886 0.040529
As it's clearly evident in the above table that some of the values in the column mad and median are very big(outliers). So i want to remove the rows which have these very big values.
For example in row3 the value of mad is 30.408377 which very big so i want to drop this row. I know that i can use one line
to remove these values from the columns but it doesn't removes the complete row
df[np.abs(df.mad-df.mad.mean()) <= (3*df.mad.std())]
But i want to remove the complete row.
How can i do that?
Predicates like what you've given will remove entire rows. But none of your data is outside of 3 standard deviations. If you tone it down to just one standard deviation, rows are removed with your example data.
Here's an example using your data:
import pandas as pd
import numpy as np
columns = ["wave", "mean", "median", "mad"]
data = [
[4050.32, -0.016182, -0.011940, 0.008885],
[4208.98, 0.023707, 0.007189, 0.032585],
[4508.28, 3.662293, 0.001414, 7.193139],
[4531.62, -15.459313, -0.001523, 30.408377],
[4551.65, 0.009028, 0.007581, 0.005247],
[4554.46, 0.001861, 0.010692, 0.027969],
[6828.60, -10.604568, -0.000590, 21.084799],
[6839.84, -0.003466, -0.001870, 0.010169],
[6842.04, -32.751551, -0.002514, 65.118329],
[6842.69, 18.293519, -0.002158, 36.385884],
[6843.66, 0.006386, -0.002468, 0.034995],
[6855.72, 0.020803, 0.000886, 0.040529],
]
df = pd.DataFrame(np.array(data), columns=columns)
print("ORIGINAL: ")
print(df)
print()
res = df[np.abs(df['mad']-df['mad'].mean()) <= (df['mad'].std())]
print("REMOVED: ")
print(res)
this outputs:
ORIGINAL:
wave mean median mad
0 4050.32 -0.016182 -0.011940 0.008885
1 4208.98 0.023707 0.007189 0.032585
2 4508.28 3.662293 0.001414 7.193139
3 4531.62 -15.459313 -0.001523 30.408377
4 4551.65 0.009028 0.007581 0.005247
5 4554.46 0.001861 0.010692 0.027969
6 6828.60 -10.604568 -0.000590 21.084799
7 6839.84 -0.003466 -0.001870 0.010169
8 6842.04 -32.751551 -0.002514 65.118329
9 6842.69 18.293519 -0.002158 36.385884
10 6843.66 0.006386 -0.002468 0.034995
11 6855.72 0.020803 0.000886 0.040529
REMOVED:
wave mean median mad
0 4050.32 -0.016182 -0.011940 0.008885
1 4208.98 0.023707 0.007189 0.032585
2 4508.28 3.662293 0.001414 7.193139
3 4531.62 -15.459313 -0.001523 30.408377
4 4551.65 0.009028 0.007581 0.005247
5 4554.46 0.001861 0.010692 0.027969
6 6828.60 -10.604568 -0.000590 21.084799
7 6839.84 -0.003466 -0.001870 0.010169
10 6843.66 0.006386 -0.002468 0.034995
11 6855.72 0.020803 0.000886 0.040529
Observe that rows indexed 8 and 9 are now gone.
Be sure you're reassigning the output of df[np.abs(df['mad']-df['mad'].mean()) <= (df['mad'].std())] as shown above. The operation is not done in place.
Doing df[np.abs(df.mad-df.mad.mean()) <= (3*df.mad.std())] will not change the dataframe.
But assign it back to df, so that:
df = df[np.abs(df.mad-df.mad.mean()) <= (3*df.mad.std())]
I have a dataframe that looks like the following. The rightmost two columns are my desired columns:
Open Close open_to_close close_to_next_open open_desired close_desired
0 0 0 3 0 0
0 0 4 8 3 7
0 0 1 1 15 16
The calculations are as the following:
open_desired = close_desired(prior row) + close_to_next_open(prior row)
close_desired = open_desired + open_to_close
How do I implement the following in a loop manner? I am trying to do this until the last row.
df = pd.DataFrame({'open': [0,0,0], 'close': [0,0,0], 'open_to_close': [0,4,1], 'close_to_next_open': [3,8,1]})
df['close_desired'] = 0
df['open_desired'] = 0
##First step is to create open_desired in current row which is dependent on close_desired in previous row
df['open_desired'] = df['close_desired'].shift() + df['close_to_next_open'].shift()
##second step is to create close_desired in current row which is dependent on open_desired in current row
df['close_desired'] = df['open_desired'] + df['open_to_close']
df.fillna(0,inplace=True)
The only way I can think of doing this is with iterrows()
for row, v in df.iterrows():
if row>0:
df.loc[row,'open_desired'] = df.shift(1).loc[row, 'close_desired'] + df.shift(1).loc[row, 'close_to_next_open']
df.loc[row,'close_desired'] = df.loc[row, 'open_desired'] + df.loc[row, 'open_to_close']
I'm new to pandas and am trying to create a pivot table from a numpy array.
variable npArray is just that, a numpy array:
>>> npArray
array([(1, 3), (4, 3), (1, 3), ..., (1, 4), (1, 12), (1, 12)],
dtype=[('MATERIAL', '<i4'), ('DIVISION', '<i4')])
I'd to count occurrences of each material by division, with division being rows and material being columns. Example:
What I have:
#numpy array to pandas data frame
pandaDf = pandas.DataFrame (npArray)
#pivot table - guessing here
pandas.pivot_table (pandaDf, index = "DIVISION",
columns = "MATERIAL",
aggfunc = numpy.sum) #<--- want count, not sum
Results:
Empty DataFrame
Columns: []
Index: []
Sample of pandaDf:
>>> print pandaDf
MATERIAL DIVISION
0 1 3
1 4 3
2 1 3
3 1 3
4 1 3
5 1 3
6 1 3
7 1 3
8 1 3
9 1 3
10 1 3
11 1 3
12 4 3
... ... ...
3845291 1 4
3845292 1 4
3845293 1 4
3845294 1 12
3845295 1 12
[3845296 rows x 2 columns]
Any help would be appreciated.
Something similar has already been asked: https://stackoverflow.com/a/12862196/9754169
Bottom line, just do aggfunc=lambda x: len(x)
#GerardoFlores is correct. Another solution I found was adding a column for frequency.
#numpy array to pandas data frame
pandaDf = pandas.DataFrame (npArray)
print "adding frequency column"
pandaDf ["FREQ"] = 1
#pivot table
pivot = pandas.pivot_table (pandaDf, values = "FREQ",
index = "DIVISION", columns = "MATERIAL",
aggfunc = "count")
I have multiple csv files with the same format (14 rows 4 columns).
I tried to load all of them into a single dataFrame, and use file's name to rename the values of the first column (1-14)
1 500 0 0
2 350 0 1
3 500 1 0
.............
13 600 0 0
14 800 0 0
I tried the following code but I am not getting what I am expecting:
filenames = os.listdir('Threshold/')
Y = pd.DataFrame () #empty df
# file name are in the following foramt "subx_ICA_thre.csv"
# need to get x (subject number to be used later for renaming columns values)
Sub_list=[]
for filename in filenames:
s= int(''.join(filter(str.isdigit, filename)))
Sub_list.append(int(s))
S_Sub_list= sorted(Sub_list)
for x in S_Sub_list: # get the file according to the subject number
temp = pd.read_csv('sub' +str(x)+'_ICA_thre.csv' )
df = pd.concat([Y, temp]) # concat the obtained frame with the empty frame
df.columns = ['id', 'data', 'isEB', 'isEM']
# replace the column values using subject id
for sub in range(1,15):
df['id'].replace(sub, 'sub' +str(x)+'_ICA_'+str(sub) ,inplace=True)
print (df)
output:
id data isEB isEM
0 sub1_ICA_2 200 0 0
1 sub1_ICA_3 275 0 0
2 sub1_ICA_4 500 1 0
................................
11 sub1_ICA_13 275 0 0
12 sub1_ICA_14 300 0 0
id data isEB isEM
0 sub2_ICA_2 275 0 0
1 sub2_ICA_3 500 0 0
2 sub2_ICA_4 400 0 0
.................................
11 sub2_ICA_13 300 0 0
12 sub2_ICA_14 450 0 0
First, it seems that the code makes different dataFrame not a single one.Second, the first row is removed (sub1_ICA_1 is missing, may be replaced with column names).
I couldn't find the problem in the loop that I am using
I think you need create list of DataFrames first, then concat with parameter keys for new values by range in MultiIndex, then modify column id and last remove MultiIndex by reset_index:
Also was added parameter names to read_csv for custom columns names.
Y = []
for x in S_Sub_list:
n = ['id', 'data', 'isEB', 'isEM']
temp = pd.read_csv('sub' + str(x) +'_ICA_thre.csv', names = n)
Y.append(temp)
#list comprehension alternative
#n = ['id', 'data', 'isEB', 'isEM']
#Y = [pd.read_csv('sub' + str(x) +'_ICA_thre.csv', names = n) for x in S_Sub_list]
df = pd.concat(Y, keys=range(1,len(S_Sub_list) + 1))
df['id'] = 'sub' + df.index.get_level_values(0).astype(str) +'_ICA_'+ df['id'].astype(str)
df = df.reset_index(drop=True)
I have an issue with keeping some data from duplicates and wanting to add valuable information to a new column in the dataframe.
import pandas as pd
data = {'id':[1,1,2,2,3],'key':[1,1,2,2,1],'value0':['a', 'b', 'x', 'y', 'a']}
frame = pd.DataFrame(data, columns = ['id','key','value0'])
print frame
Yields:
id key value0
0 1 1 a
1 1 1 b
2 2 2 x
3 2 2 y
4 3 1 a
Desired Output:
key value0_0 value0_1 value1_0
0 1 a b a
1 2 x y None
The "id" column isn't important to keep but could help with iteration and grouping.
I think this could be adapted to other projects where you don't know how many values exist for a set of keys.
set_index including a cumcount and unstack
frame.set_index(
['key', frame.groupby('key').cumcount()]
).value0.unstack().add_prefix('value0_').reset_index()
key value0_0 value0_1 value0_2
0 1 a b a
1 2 x y None
I'm questioning your column labeling but here is an approach using binary
frame.set_index(
['key', frame.groupby('key').cumcount()]
).value0.unstack().rename(
columns='{:02b}'.format
).add_prefix('value_').reset_index()
key value_00 value_01 value_10
0 1 a b a
1 2 x y None