小编ema*_*max的帖子

您好我有以下数据帧.

    Group           Size

    Short          Small
    Short          Small
    Moderate       Medium
    Moderate       Small
    Tall           Large

我想计算同一行在数据帧中出现的时间的频率.

    Group           Size      Time

    Short          Small        2
    Moderate       Medium       1 
    Moderate       Small        1
    Tall           Large        1

我正在尝试在 mac 机器上安装fiona和。geopandas

我已经安装了GDAL槽brew install gdal --HEAD

如果我尝试安装fiona，我会得到以下信息：

sudo pip3 install fiona\nCollecting fiona\n  Downloading Fiona-1.8.18.tar.gz (1.3 MB)\n     |\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88| 1.3 MB 4.7 MB/s \n    ERROR: Command errored out with exit status 1:\n     command: /usr/local/opt/python@3.9/bin/python3.9 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/tmp/pip-install-sk0rqerp/fiona_e389978ed5a44635a5158a0ba3df8562/setup.py'"'"'; __file__='"'"'/private/tmp/pip-install-sk0rqerp/fiona_e389978ed5a44635a5158a0ba3df8562/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\\r\\n'"'"', '"'"'\\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /private/tmp/pip-pip-egg-info-bngdnoq7\n         cwd: /private/tmp/pip-install-sk0rqerp/fiona_e389978ed5a44635a5158a0ba3df8562/\n    Complete output (2 lines):\n    Failed to get options via gdal-config: [Errno 2] No such file or directory: …

我有一个如下表

    df

 +------------------------------------+-----------------------+
|identifier                          |timestamp              |
+------------------------------------+-----------------------+
|86311425-0890-40a5-8950-54cbaaa60815|2020-03-18 14:41:55 UTC|
|38e121a8-f21f-4d10-bb69-26eb045175b5|2020-03-13 15:19:21 UTC|
|1a69c9b0-283b-4b6d-89ac-66f987280c66|2020-03-16 12:59:51 UTC|
|c7b5c53f-bf40-498f-8302-4b3329322bc9|2020-03-18 22:05:06 UTC|
|0d3d807b-9b3a-466e-907c-c22402240730|2020-03-17 18:40:03 UTC|
+------------------------------------+-----------------------+

tmp.printSchema()
root
 |-- identifier: string (nullable = true)
 |-- timestamp: string (nullable = true)

我想要一个只包含时间戳中的日期和小时的列。

我正在尝试以下操作：

from pyspark.sql.functions import hour
df = df.withColumn("hour", hour(col("timestamp")))

但我得到以下结果

+--------------------+--------------------+----+
|          identifier|           timestamp|hour|
+--------------------+--------------------+----+
|321869c3-71e5-41d...|2020-03-19 03:34:...|null|
|226b8d50-2c6a-471...|2020-03-19 02:59:...|null|
|47818b7c-34b5-43c...|2020-03-19 01:41:...|null|
|f5ca5599-7252-49d...|2020-03-19 04:25:...|null|
|add2ae24-aa7b-4d3...|2020-03-19 01:50:...|null|
+--------------------+--------------------+----+

虽然我想拥有

+--------------------+--------------------+-------------------+
|          identifier|           timestamp|hour               |
+--------------------+--------------------+-------------------+
|321869c3-71e5-41d...|2020-03-19 03:00:...|2020-03-19 03:00:00|
|226b8d50-2c6a-471...|2020-03-19 02:59:...|2020-03-19 02:00:00|
|47818b7c-34b5-43c...|2020-03-19 01:41:...|2020-03-19 01:00:00| …

您好我有以下数据帧

df = 

       Record_ID       Time
        94704   2014-03-10 07:19:19.647342
        94705   2014-03-10 07:21:44.479363
        94706   2014-03-10 07:21:45.479581
        94707   2014-03-10 07:21:54.481588
        94708   2014-03-10 07:21:55.481804

有可能有以下吗？

df1 = 

       Record_ID       Time
        94704   2014-03-10 07:19:19
        94705   2014-03-10 07:21:44
        94706   2014-03-10 07:21:45
        94707   2014-03-10 07:21:54
        94708   2014-03-10 07:21:55

我将一个.csv文件作为数据框读取，如下所示：

import pandas as pd
df = pd.read_csv('myFile.csv')
df.head()
    BoroName    geometry
0   Brooklyn    MULTIPOLYGON (((-73.97604935657381 40.63127590...
1   Queens      MULTIPOLYGON (((-73.80379022888098 40.77561011...
2   Queens      MULTIPOLYGON (((-73.8610972440186 40.763664477...
3   Queens      MULTIPOLYGON (((-73.75725671509139 40.71813860...
4   Manhattan   MULTIPOLYGON (((-73.94607828674226 40.82126321...

我想将其转换为 geopandas 数据框。

import geopandas as gpd
crs = {'init': 'epsg:4326'}
gdf = gpd.GeoDataFrame(df, crs=crs).set_geometry('geometry')

但我收到以下错误

TypeError: Input must be valid geometry objects: MULTIPOLYGON (((-73.97604935657381 40.631275905646774, -73.97716511994669 40.63074665412933,....

我有一个如下所示的数据框

df = 
    a   ID1         ID2         Proximity
0   0   900000498   NaN         0.000000
1   1   900000498   900004585   3.900000
2   2   900000498   900005562   3.900000
3   3   900000498   900008613   0.000000
4   4   900000498   900012333   0.000000
5   5   900000498   900019524   3.900000
6   6   900000498   900019877   0.000000
7   7   900000498   900020141   3.900000
8   8   900000498   900022133   3.900000
9   9   900000498   900022919   0.000000

我想为给定的夫妇找到ID1-ID2相应的Proximity值。例如，给定[900000498, 900022133]我想要的输入作为输出3.900000

我正在使用以下过程在子图中绘制不同的图形。

fig = figure(figsize=(10,11))
subplots_adjust(wspace=0.5,hspace=0.2)
iplot = 330
for i in range(9):
    iplot += 1
    ax = fig.add_subplot(iplot)
    ## Comparison Mreal - Real
    tmp = REAL[REAL.days==days[i]]
    tmp = tmp.score
    tmp = np.array(tmp)
    tmp = tmp.reshape(len(xv), len(br))
    im = plt.imshow(tmp, interpolation='nearest', cmap='gnuplot', vmin = 0, vmax = 1,  extent=[0.05,0.5,1,0.05],
              aspect=0.5)
    xtmp = [0.05, 0.2, 0.3, 0.4, 0.5]
    plt.xticks(xtmp)
    ytmp = [0.05, 0.2, 0.4, 0.6, 0.8, 1.0]
    plt.yticks(ytmp)
    ax.grid(False)
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "5%", pad="3%")
plt.colorbar(im, cax=cax)
plt.tight_layout()

这就是我得到的：

但是，我希望所有子图都具有相同的颜色条，例如在图的右侧。

我有一个数据df1框,对应nodes于网络中的egelist 和value节点本身,如下所示:

df
    node_i    node_j    value_i   value_j
0    3         4          89         33
1    3         2          89         NaN
2    3         5          89         69
3    0         2          45         NaN
4    0         3          45         89
5    1         2          109        NaN
6    1         8          109        NaN

我想添加一个w对应于value_jif是否有值的列.如果value_j是NaN我想设置w作为平均的相邻节点的值的i.在node_i仅具有NaN设置值的相邻节点的情况下w=1.

所以最终的数据框应该像愚弄:

df
    node_i    node_j    value_i   value_j      w
0    3         4          89         33       33 …

我生成了一个图表networkx

import networkx as nx  
s = 5
G = nx.grid_graph(dim=[s,s])
nodes = list(G.nodes)
edges = list(G.edges)
p = []
for i in range(0, s):
    for j in range(0, s):
        p.append([i,j])
for i in range(0, len(nodes)):
    G.nodes[nodes[i]]['pos'] = p[i]

pos = {}
for i in range(0, len(nodes)):
        pos[nodes[i]] = p[i]

nx.draw(G, pos)

现在我想为0和之间的每个节点分配一个值4

from random import randint
val = []
for i in range(0, len(G.nodes())):
    val.append(randint(0,4)) 

我想为列表中的每个节点分配颜色并绘制如下val所示的内容

我有一些来自gps数据(Lat,Lon)和shapefile我正在考虑的区域的相对数据点.所以:

import pandas as pd
import shapefile
sf = shapefile.Reader('roadshape')
df = pd.read_csv('gpsdata.csv')

现在df包含我正在分析的数据是这样的:

 ID          x          y
3447    11.400427   48.816806
3448    11.400759   48.816772
3449    11.401424   48.816684
3450    11.401758   48.816631
3451    11.402090   48.816566
3452    11.402422   48.816490

我想分配给每个相应的shapefile细分市场.我正在尝试执行以下操作.我正在考虑边界框,我想尝试查看数据指向数据栏的边界框.

dfs = pd.DataFrame()
shapes = sf.shapes()
X =list()
Y=list()
for i in range(0,len(shapes)):
      X.append([shapes[i].bbox[0],shapes[i].bbox[2]])
      Y.append([shapes[i].bbox[1],shapes[i].bbox[3]])
dfs['X'] = X
dfs['Y'] = Y

现在我该如何查看我的积分是哪个bbox？哪里dfs是这样的

dfs = 
                 X                             Y
0   [10.9467244189, 10.9704393002]  [48.2671975178, 48.2697440003]
1   [11.5138847999, 11.5143541004]  [48.6497096997, 48.6515363002] …