1. If the dataframe (say df) wholly consists of float64 dtypes, you can do:

df = df.astype('float32')

2. Only if some columns are float64, then you'd have to select those columns and change their dtype:

# Select columns with 'float64' dtype  
float64_cols = list(df.select_dtypes(include='float64'))

# The same code again calling the columns
df[float64_cols] = df[float64_cols].astype('float32')

I think this does what you want:

pd.read_csv('Filename.csv').dropna().astype(np.float32)

To keep rows that only have some NaN values, do this:

pd.read_csv('Filename.csv').dropna(how='all').astype(np.float32)

To replace each NaN with a number instead of dropping rows, do this:

pd.read_csv('Filename.csv').fillna(1e6).astype(np.float32)

(I replaced NaN with 1,000,000 just as an example.)

Many decimal floating point numbers can not be accurately represented with a float64 or float32. Review e.g. The Floating-Point Guide if you are unfamiliar with that issue.

Pandas defaults to displaying floating points with a precision of 6, and trailing 0s are dropped in the default output.

float64 can accurately represent the example numbers up to (and beyond) precision 6, whereas float32 can not:

>>> print("%.6f" % np.float64(49.40))
49.400000

>>> print("%.6f" % np.float32(49.40))
49.400002

If you are not interested in the precision beyond the 2nd digit when printing the df, you can set the display precision:

pd.set_option('precision', 2)

Then you get the same output even with float32s:

 >>> df.astype(np.float32)
                     600663.XSHG  000877.XSHE  600523.XSHG  601311.XSHG
2016-01-04 09:31:00        49.40         8.05        22.79        21.80
           09:32:00        49.55         8.03        22.79        21.75

If you want to drop everything beyond the 2nd digit when writing back the csv file, use float_format:

df.to_csv(file_name, float_format="%.2f")

You can use to_numeric which will give you a float32

or np.float32(x)

then x.item() will give you python float

The problem is that you do not do any type conversion of the numpy array. You calculate a float32 variable and put it as an entry into a float64 numpy array. numpy then converts it properly back to float64

Try someting like this:

a = np.zeros(4,dtype="float64") 
print a.dtype
print type(a[0])
a = np.float32(a)
print a.dtype
print type(a[0])

The output (tested with python 2.7)

float64
<type 'numpy.float64'>
float32
<type 'numpy.float32'>

a is in your case the array tree.tree_.threshold

Use numpy.float32:

In [320]:
import numpy as np
import pandas as pd
df = pd.DataFrame({'a':np.random.randn(10)})
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 1 columns):
a    10 non-null float64
dtypes: float64(1)
memory usage: 160.0 bytes

In [323]:   
df['a'].astype(np.float32)

Out[323]:
0    0.966618
1   -0.331942
2    0.906349
3   -0.089582
4   -0.722004
5    0.668103
6    0.230314
7   -1.707631
8    1.806862
9    1.783765
Name: a, dtype: float32

You can see that the dtype is now float32

You can convert most of the columns by just calling convert_objects:

In [36]:

df = df.convert_objects(convert_numeric=True)
df.dtypes
Out[36]:
Date         object
WD            int64
Manpower    float64
2nd          object
CTR          object
2ndU        float64
T1            int64
T2          int64
T3           int64
T4        float64
dtype: object

For column '2nd' and 'CTR' we can call the vectorised str methods to replace the thousands separator and remove the '%' sign and then astype to convert:

In [39]:

df['2nd'] = df['2nd'].str.replace(',','').astype(int)
df['CTR'] = df['CTR'].str.replace('%','').astype(np.float64)
df.dtypes
Out[39]:
Date         object
WD            int64
Manpower    float64
2nd           int32
CTR         float64
2ndU        float64
T1            int64
T2            int64
T3            int64
T4           object
dtype: object
In [40]:

df.head()
Out[40]:
        Date  WD  Manpower   2nd   CTR  2ndU   T1  　　T2   T3     T4
0   2013/4/6   6       NaN  2645  5.27  0.29  407   533  454    368
1   2013/4/7   7       NaN  2118  5.89  0.31  257   659  583    369
2  2013/4/13   6       NaN  2470  5.38  0.29  354   531  473  　　383
3  2013/4/14   7       NaN  2033  6.77  0.37  396   748  681    458
4  2013/4/20   6       NaN  2690  5.38  0.29  361   528  541    381

Or you can do the string handling operations above without the call to astype and then call convert_objects to convert everything in one go.

UPDATE

Since version 0.17.0 convert_objects is deprecated and there isn't a top-level function to do this so you need to do:

df.apply(lambda col:pd.to_numeric(col, errors='coerce'))

See the docs and this related question: pandas: to_numeric for multiple columns