I found an interesting snippet (vrana95) that caps multiple columns, however this function works on the main "df" as well instead to work only on "final_df". Someone knows why?
def cap_data(df):
for col in df.columns:
print("capping the ",col)
if (((df[col].dtype)=='float64') | ((df[col].dtype)=='int64')):
percentiles = df[col].quantile([0.01,0.99]).values
df[col][df[col] <= percentiles[0]] = percentiles[0]
df[col][df[col] >= percentiles[1]] = percentiles[1]
else:
df[col]=df[col]
return df
final_df=cap_data(df)
As I wanted to cap only a few columns I changed the for loop of the original snippet. It works, but I would to know why this function is working with both dataframes.
cols = ['score_3', 'score_6', 'credit_limit', 'last_amount_borrowed', 'reported_income', 'income']
def cap_data(df):
for col in cols:
print("capping the column:",col)
if (((df[col].dtype)=='float64') | ((df[col].dtype)=='int64')):
percentiles = df[col].quantile([0.01,0.99]).values
df[col][df[col] <= percentiles[0]] = percentiles[0]
df[col][df[col] >= percentiles[1]] = percentiles[1]
else:
df[col]=df[col]
return df
final_df=cap_data(df)
for every city , I want to create a new column which is minmax scalar of another columns (age).
I tried this an get Input contains infinity or a value too large for dtype('float64').
cols=['age']
def f(x):
scaler1=preprocessing.MinMaxScaler()
x[['age_minmax']] = scaler1.fit_transform(x[cols])
return x
df = df.groupby(['city']).apply(f)
From the comments:
df['age'].replace([np.inf, -np.inf], np.nan, inplace=True)
Or
df['age'] = df['age'].replace([np.inf, -np.inf], np.nan)
I am trying to apply a function to every column in a dataframe, when I try to do it on just a single fixed column name it works. I tried doing it on every column, but when I try passing the column name as an argument in the function I get an error.
How do you properly pass arguments to apply a function on a data frame?
def result(row,c):
if row[c] >=0 and row[c] <=1:
return 'c'
elif row[c] >1 and row[c] <=2:
return 'b'
else:
return 'a'
cols = list(df.columns.values)
for c in cols
df[c] = df.apply(result, args = (c), axis=1)
TypeError: ('result() takes exactly 2 arguments (21 given)', u'occurred at index 0')
Input data frame format:
d = {'c1': [1, 2, 1, 0], 'c2': [3, 0, 1, 2]}
df = pd.DataFrame(data=d)
df
c1 c2
0 1 3
1 2 0
2 1 1
3 0 2
You don't need to pass the column name to apply. As you only want to check if values of the columns are in certain range and should return a, b or c. You can make the following changes.
def result(val):
if 0<=val<=1:
return 'c'
elif 1<val<=2:
return 'b'
return 'a'
cols = list(df.columns.values)
for c in cols
df[c] = df[c].apply(result)
Note that this will replace your column values.
A faster way is np.select:
import numpy as np
values = ['c', 'b']
for col in df.columns:
df[col] = np.select([0<=df[col]<=1, 1<df[col]<=2], values, default = 'a')
I have some time periods (df_A) and some time instants (df_B):
import pandas as pd
import numpy as np
import datetime as dt
from datetime import timedelta
# Data
df_A = pd.DataFrame({'A1': [dt.datetime(2017,1,5,9,8), dt.datetime(2017,1,5,9,9), dt.datetime(2017,1,7,9,19), dt.datetime(2017,1,7,9,19), dt.datetime(2017,1,7,9,19), dt.datetime(2017,2,7,9,19), dt.datetime(2017,2,7,9,19)],
'A2': [dt.datetime(2017,1,5,9,9), dt.datetime(2017,1,5,9,12), dt.datetime(2017,1,7,9,26), dt.datetime(2017,1,7,9,20), dt.datetime(2017,1,7,9,21), dt.datetime(2017,2,7,9,23), dt.datetime(2017,2,7,9,25)]})
df_B = pd.DataFrame({ 'B': [dt.datetime(2017,1,6,14,45), dt.datetime(2017,1,4,3,31), dt.datetime(2017,1,7,3,31), dt.datetime(2017,1,7,14,57), dt.datetime(2017,1,9,14,57)]})
I can match these together:
# Define an Extra Margin
M = dt.timedelta(days = 10)
df_A["A1X"] = df_A["A1"] + M
df_A["A2X"] = df_A["A2"] - M
# Match
Bv = df_B .B .values
A1 = df_A .A1X.values
A2 = df_A .A2X.values
i, j = np.where((Bv[:, None] >= A1) & (Bv[:, None] <= A2))
df_C = pd.DataFrame(np.column_stack([df_B .values[i], df_A .values[j]]),
columns = df_B .columns .append (df_A.columns))
I would like to find the time difference between each time period and the time instant matched to it. I mean that
if B is between A1 and A2
then dT = 0
I've tried doing it like this:
# Calculate dt
def time(A1,A2,B):
if df_C["B"] < df_C["A1"]:
return df_C["A1"].subtract(df_C["B"])
elif df_C["B"] > df_C["A2"]:
return df_C["B"].subtract(df_C["A2"])
else:
return 0
df_C['dt'] = df_C.apply(time)
I'm getting "ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series"
So, I found two fixes:
You are adding M to the lower value and subtracting from the higher one. Change it to:
df_A['A1X'] = df_A['A1'] - M
df_A['A2X'] = df_A['A2'] + M
You are only passing one row of your dataframe at a time to your time function, so it should be something like:
def time(row):
if row['B'] < row['A1']:
return row['A1'] - row['B']
elif row['B'] > row['A2']:
return row['B'] - row['A2']
else:
return 0
And then you can call it like this:
df_C['dt'] = df_C.apply(time, axis=1) :)
How to use the values of one column to access values in another
import numpy
impot pandas
numpy.random.seed(123)
df = pandas.DataFrame((numpy.random.normal(0, 1, 10)), columns=[['Value']])
df['bleh'] = df.index.to_series().apply(lambda x: numpy.random.randint(0, x + 1, 1)[0])
so how to access the value 'bleh' for each row?
df.Value.iloc[df['bleh']]
Edit:
Thanks to #ScottBoston. My DF constructor had one layer of [] too much.
The correct answer is:
numpy.random.seed(123)
df = pandas.DataFrame((numpy.random.normal(0, 1, 10)), columns=['Value'])
df['bleh'] = df.index.to_series().apply(lambda x: numpy.random.randint(0, x + 1, 1)[0])
df['idx_int'] = range(df.shape[0])
df['haa'] = df['idx_int'] - df.bleh.values
df['newcol'] = df.Value.iloc[df['haa'].values].values
Try:
df['Value'].tolist()
Output:
[-1.0856306033005612,
0.9973454465835858,
0.28297849805199204,
-1.506294713918092,
-0.5786002519685364,
1.651436537097151,
-2.426679243393074,
-0.42891262885617726,
1.265936258705534,
-0.8667404022651017]
Your dataframe constructor still needs to be fixed.
Are you looking for:
df.set_index('bleh')
output:
Value
bleh
0 -1.085631
1 0.997345
2 0.282978
1 -1.506295
4 -0.578600
0 1.651437
0 -2.426679
4 -0.428913
1 1.265936
7 -0.866740
If so you, your dataframe constructor has as extra set of [] in it.
np.random.seed(123)
df = pd.DataFrame((np.random.normal(0, 1, 10)), columns=['Value'])
df['bleh'] = df.index.to_series().apply(lambda x: np.random.randint(0, x + 1, 1)[0])
columns paramater in dataframe takes a list not a list of list.