CODEWITHSUNDEEP
pythonpandasseries
Martijn van Amsterdam
Martijn van Amsterdam

Reputation: 326

Counting the amount of times a boolean goes from True to False in a column

I have a column in a dataframe which is filled with booleans and i want to count how many times it changes from True to False.

I can do this when I convert the booleans to 1's and 0's ,then use df.diff and then divide that answer by 2

import pandas as pd

d = {'Col1': [True, True, True, False, False, False, True, True, True, True, False, False, False, True, True, False, False, True, ]}


df = pd.DataFrame(data=d)


print(df)

0    True
1    True
2    True
3   False
4   False
5   False
6    True
7    True
8    True
9    True
10  False
11  False
12  False
13   True
14   True
15  False
16  False

My expected outcome would be The amount of times False came up is 3

Upvotes: 9

Views: 2654

Answers (5)

yatu
yatu

Reputation: 88276

You can perform a bitwise and of the Col1 with a mask indicating where changes occur in successive rows:

(df.Col1 & (df.Col1 != df.Col1.shift(1))).sum()
3

Where the mask, is obtained by comparing Col1 with a shifted version of itself (pd.shift):

df.Col1 != df.Col1.shift(1)

0      True
1     False
2     False
3      True
4     False
5     False
6      True
7     False
8     False
9     False
10     True
11    False
12    False
13     True
14    False
15    False
16    False
17    False
Name: Col1, dtype: bool

For multiple columns, you can do exactly the same (Here I tested with a col2 identical to col1)

(df & (df != df.shift(1))).sum()

Col1    3
Col2    3
dtype: int64

Upvotes: 7

alec_djinn
alec_djinn

Reputation: 10809

Less concise but perhaps a more readable approach would be:

count = 0
for item in zip(d['Col1'], d['Col1'][1:]):
    if item == (True, False):
        count += 1
print(count)

Upvotes: 1

Hayden McCormick
Hayden McCormick

Reputation: 211

My strategy was to find where the difference in one row to the next. (Considering that Trues are 1's and Falses are 0's, of course.)

Thus, Colm1 - Colm1.shift() represents the Delta value where a 1 is a shift from False to True, 0 No Change, and -1 shift from True to False. 

import pandas as pd

d = {'Col1': [True, True, True, False, False, False, True, True, True, True, False, False, False, True, True, False, False, True, ]}

df = pd.DataFrame(data=d)
df['delta'] = df['Col1'] - df['Col1'].shift()
BooleanShifts = df['delta'].value_counts()
print(BooleanShifts[-1])

After getting the value counts as a dict of these [1, 0, -1] values, you can select for just the -1's and get the number of times the DF shifted to a False Value from a True Value. I hope this helped answer your question!

Upvotes: 1

jpp
jpp

Reputation: 164773

Notice that subtracting True (1) from False (0) in integer terms gives -1:

res = df['Col1'].astype(int).diff().eq(-1).sum()  # 3

To apply across a Boolean dataframe, you can construct a series mapping label to count:

res = df.astype(int).diff().eq(-1).sum()

Upvotes: 4

BENY
BENY

Reputation: 323316

Just provide different idea 

df.cumsum()[~df.Col1].nunique()
Out[408]: 
Col1    3
dtype: int64

Upvotes: 2

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