Pandas DataFrame use previous row value for complicated 'if' conditions to determine current value

Question

I want to know if there is any faster way to do the following loop? Maybe use apply or rolling apply function to realize this Basically, I need to access previous row's value to determine current cell value.

df.ix[0] = (np.abs(df.ix[0]) >= So) * np.sign(df.ix[0])
for i in range(1, len(df)):
    for col in list(df.columns.values):
        if ((df[col].ix[i] > 1.25) & (df[col].ix[i-1] == 0)) | :
            df[col].ix[i] = 1
        elif ((df[col].ix[i] < -1.25) & (df[col].ix[i-1] == 0)):
            df[col].ix[i] = -1
        elif ((df[col].ix[i] <= -0.75) & (df[col].ix[i-1] < 0)) | ((df[col].ix[i] >= 0.5) & (df[col].ix[i-1] > 0)):
            df[col].ix[i] = df[col].ix[i-1]
        else:
            df[col].ix[i] = 0

As you can see, in the function, I am updating the dataframe, I need to access the most updated previous row, so using shift will not work.

For example: Input:

A      B     C
1.3  -1.5   0.7
1.1  -1.4   0.6
1.0  -1.3   0.5
0.4   1.4   0.4

Output:

 A      B     C
1     -1      0
1     -1      0
1     -1      0
0      1      0

Answer 1

you can use .shift() function for accessing previous or next values:

previous value for col column:

df['col'].shift()

next value for col column:

df['col'].shift(-1)

Example:

In [38]: df
Out[38]:
   a  b  c
0  1  0  5
1  9  9  2
2  2  2  8
3  6  3  0
4  6  1  7

In [39]: df['prev_a'] = df['a'].shift()

In [40]: df
Out[40]:
   a  b  c  prev_a
0  1  0  5     NaN
1  9  9  2     1.0
2  2  2  8     9.0
3  6  3  0     2.0
4  6  1  7     6.0

In [43]: df['next_a'] = df['a'].shift(-1)

In [44]: df
Out[44]:
   a  b  c  prev_a  next_a
0  1  0  5     NaN     9.0
1  9  9  2     1.0     2.0
2  2  2  8     9.0     6.0
3  6  3  0     2.0     6.0
4  6  1  7     6.0     NaN